The second GravitationalWave Transient Catalog GWTC2 reported on 39 compact binary coalescences observed by the Advanced LIGO and Advanced Virgo detectors between 1 April 2019 inlineformulammlmath displayinlinemmlmrowmmlmn15mmlmnmmlmommlmommlmn00mmlmnmmlmrowmmlmathinlineformula UTC and 1 October 2019 inlineformulammlmath displayinlinemmlmrowmmlmn15mmlmnmmlmommlmommlmn00mmlmnmmlmrowmmlmathinlineformula UTC. Here we present GWTC2.1 which reports on a deeper list of candidate events observed over the same period. We analyze the final version of the strain data over this period with improved calibration and better subtraction of excess noise which has been publicly released. We employ three matchedfilter search pipelines for candidate identification and estimate the probability of astrophysical origin for each candidate event. While GWTC2 used a false alarm rate threshold of 2 per year we include in GWTC2.1 1201 candidates that pass a false alarm rate threshold of 2 per day. We calculate the source properties of a subset of 44 highsignificance candidates that have a probability of astrophysical origin greater than 0.5. Of these candidates 36 have been reported in GWTC2. We also calculate updated source properties for all binary black hole events previously reported in GWTC1. If the eight additional highsignificance candidates presented here are astrophysical the mass range of events that are unambiguously identified as binary black holes both objects inlineformulammlmath displayinlinemmlmommlmommlmn3mmlmnmmlmsubmmlmiMmmlmimmlmo stretchyfalsemmlmommlmsubmmlmathinlineformula is increased compared to GWTC2 with total masses from inlineformulammlmath displayinlinemmlmommlmommlmn14mmlmnmmlmsubmmlmiMmmlmimmlmo stretchyfalsemmlmommlmsubmmlmathinlineformula for GW190924021846 to inlineformulammlmath displayinlinemmlmommlmommlmn182mmlmnmmlmsubmmlmiMmmlmimmlmo stretchyfalsemmlmommlmsubmmlmathinlineformula for GW190426190642. Source properties calculated using our default prior suggest that the primary components of two new candidate events GW190403051519 and GW190426190642 fall in the mass gap predicted by pairinstability supernova theory. We also expand the population of binaries with significantly asymmetric mass ratios reported in GWTC2 by an additional two events the mass ratio is less than 0.65 and 0.44 at 90 probability for GW190403051519 and GW190917114630 respectively and find that two of the eight new events have effective inspiral spins inlineformulammlmath displayinlinemmlmsubmmlmimmlmimmlmieffmmlmimmlmsubmmlmogtmmlmommlmn0mmlmnmmlmathinlineformula at 90 credibility while no binary is consistent with inlineformulammlmath displayinlinemmlmsubmmlmimmlmimmlmieffmmlmimmlmsubmmlmoltmmlmommlmn0mmlmnmmlmathinlineformula at the same significance. We provide updated estimates for rates of binary black hole and binary neutron star coalescence in the local Universe.
['Experiments in gravity', 'cosmology', 'cosmic rays', 'General Relativity and Quantum Cosmology']
GWTC2.1 Deep extended catalog of compact binary coalescences observed by LIGO and Virgo during the first half of the third observing run
{'GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run': "R. Abbott, 1 T. D. Abbott, 2 F. Acernese, 3, 4 K. Ackley, 5 C. Adams, 6 N. Adhikari, 7 R. X. Adhikari, 1 V. B. Adya, 8 C. Affeldt, 9, 10 D. Agarwal, 11 M. Agathos, 12, 13 K. Agatsuma, 14 N. Aggarwal, 15 O. D. Aguiar, 16 L. Aiello, 17 A. Ain, 18 P. Ajith, 19 S. Albanesi, 20 A. Allocca, 21, 4 P. A. Altin, 8 A. Amato, 22 C. Anand, 5 S. Anand, 1 A. Ananyeva, 1 S. B. Anderson, 1 W. G. Anderson, 7 T. Andrade, 23 N. Andres, 24 T. Andri'c, 25 S. V. Angelova, 26 S. Ansoldi, 27, 28 J. M. Antelis, 29 S. Antier, 30 S. Appert, 1 K. Arai, 1 M. C. Araya, 1 J. S. Areeda, 31 M. Ar'ene, 30 N. Arnaud, 32, 33 S. M. Aronson, 2 K. G. Arun, 34 Y. Asali, 35 G. Ashton, 5 M. Assiduo, 36, 37 S. M. Aston, 6 P. Astone, 38 F. Aubin, 24 C. Austin, 2 S. Babak, 30 F. Badaracco, 39 M. K. M. Bader, 40 C. Badger, 41 S. Bae, 42 A. M. Baer, 43 S. Bagnasco, 20 Y. Bai, 1 J. Baird, 30 M. Ball, 44 G. Ballardin, 33 S. W. Ballmer, 45 A. Balsamo, 43 G. Baltus, 46 S. Banagiri, 47 D. Bankar, 11 J. C. Barayoga, 1 C. Barbieri, 48, 49, 50 B. C. Barish, 1 D. Barker, 51 P. Barneo, 23 F. Barone, 52, 4 B. Barr, 53 L. Barsotti, 54 M. Barsuglia, 30 D. Barta, 55 J. Bartlett, 51 M. A. Barton, 53 I. Bartos, 56 R. Bassiri, 57 A. Basti, 58, 18 M. Bawaj, 59, 60 J. C. Bayley, 53 A. C. Baylor, 7 M. Bazzan, 61, 62 B. B'ecsy, 63 V. M. Bedakihale, 64 M. Bejger, 65 I. Belahcene, 32 V. Benedetto, 66 D. Beniwal, 67 T. F. Bennett, 68 J. D. Bentley, 14 M. BenYaala, 26 F. Bergamin, 9, 10 B. K. Berger, 57 S. Bernuzzi, 13 C. P. L. Berry, 15, 53 D. Bersanetti, 69 A. Bertolini, 40 J. Betzwieser, 6 D. Beveridge, 70 R. Bhandare, 71 U. Bhardwaj, 72, 40 D. Bhattacharjee, 73 S. Bhaumik, 56 I. A. Bilenko, 74 G. Billingsley, 1 S. Bini, 75, 76 R. Birney, 77 O. Birnholtz, 78 S. Biscans, 1, 54 M. Bischi, 36, 37 S. Biscoveanu, 54 A. Bisht, 9, 10 B. Biswas, 11 M. Bitossi, 33, 18 M.-A. Bizouard, 79 J. K. Blackburn, 1 C. D. Blair, 70, 6 D. G. Blair, 70 R. M. Blair, 51 F. Bobba, 80, 81 N. Bode, 9, 10 M. Boer, 79 G. Bogaert, 79 M. Boldrini, 82, 38 L. D. Bonavena, 61 F. Bondu, 83 E. Bonilla, 57 R. Bonnand, 24 P. Booker, 9, 10 B. A. Boom, 40 R. Bork, 1 V. Boschi, 18 N. Bose, 84 S. Bose, 11 V. Bossilkov, 70 V. Boudart, 46 Y. Bouffanais, 61, 62 A. Bozzi, 33 C. Bradaschia, 18 P. R. Brady, 7 A. Bramley, 6 A. Branch, 6 M. Branchesi, 25, 85 J. E. Brau, 44 M. Breschi, 13 T. Briant, 86 J. H. Briggs, 53 A. Brillet, 79 M. Brinkmann, 9, 10 P. Brockill, 7 A. F. Brooks, 1 J. Brooks, 33 D. D. Brown, 67 S. Brunett, 1 G. Bruno, 39 R. Bruntz, 43 J. Bryant, 14 T. Bulik, 87 H. J. Bulten, 40 A. Buonanno, 88, 89 R. Buscicchio, 14 D. Buskulic, 24 C. Buy, 90 R. L. Byer, 57 L. Cadonati, 91 G. Cagnoli, 22 C. Cahillane, 51 J. Calder'on Bustillo, 92, 93 J. D. Callaghan, 53 T. A. Callister, 94, 95 E. Calloni, 21, 4 J. Cameron, 70 J. B. Camp, 96 M. Canepa, 97, 69 S. Canevarolo, 98 M. Cannavacciuolo, 80 K. C. Cannon, 99 H. Cao, 67 E. Capote, 45 G. Carapella, 80, 81 F. Carbognani, 33 J. B. Carlin, 100 M. F. Carney, 15 M. Carpinelli, 101, 102, 33 G. Carrillo, 44 G. Carullo, 58, 18 T. L. Carver, 17 J. Casanueva Diaz, 33 C. Casentini, 103, 104 G. Castaldi, 105 S. Caudill, 40, 98 M. Cavagli'a, 73 F. Cavalier, 32 R. Cavalieri, 33 M. Ceasar, 106 G. Cella, 18 P. Cerd'a-Dur'an, 107 E. Cesarini, 104 W. Chaibi, 79 K. Chakravarti, 11 S. Chalathadka Subrahmanya, 108 E. Champion, 109 C.-H. Chan, 110 C. Chan, 99 C. L. Chan, 93 K. Chan, 93 K. Chandra, 84 P. Chanial, 33 S. Chao, 110 P. Charlton, 111 E. A. Chase, 15 E. Chassande-Mottin, 30 C. Chatterjee, 70 Debarati Chatterjee, 11 Deep Chatterjee, 7 D. Chattopadhyay, 112 M. Chaturvedi, 71 S. Chaty, 30 K. Chatziioannou, 1 H. Y. Chen, 54 J. Chen, 110 X. Chen, 70 Y. Chen, 113 Z. Chen, 17 H. Cheng, 56 C. K. Cheong, 93 H. Y. Cheung, 93 H. Y. Chia, 56 F. Chiadini, 114, 81 G. Chiarini, 62 R. Chierici, 115 A. Chincarini, 69 M. L. Chiofalo, 58, 18 A. Chiummo, 33 G. Cho, 116 H. S. Cho, 117 R. K. Choudhary, 70 S. Choudhary, 11 N. Christensen, 79 Q. Chu, 70 S. Chua, 8 K. W. Chung, 41 G. Ciani, 61, 62 P. Ciecielag, 65 M. Cie'slar, 65 M. Cifaldi, 103, 104 A. A. Ciobanu, 67 R. Ciolfi, 118, 62 F. Cipriano, 79 A. Cirone, 97, 69 F. Clara, 51 E. N. Clark, 119 J. A. Clark, 1, 91 L. Clarke, 120 P. Clearwater, 112 S. Clesse, 121 F. Cleva, 79 E. Coccia, 25, 85 E. Codazzo, 25 P.-F. Cohadon, 86 D. E. Cohen, 32 L. Cohen, 2 M. Colleoni, 122 C. G. Collette, 123 A. Colombo, 48 M. Colpi, 48, 49 C. M. Compton, 51 M. Constancio Jr., 16 L. Conti, 62 S. J. Cooper, 14 P. Corban, 6 T. R. Corbitt, 2 I. Cordero-Carri'on, 124 S. Corezzi, 60, 59 K. R. Corley, 35 N. Cornish, 63 D. Corre, 32 A. Corsi, 125 S. Cortese, 33 C. A. Costa, 16 R. Cotesta, 89 M. W. Coughlin, 47 J.-P. Coulon, 79 S. T. Countryman, 35 B. Cousins, 126 P. Couvares, 1 D. M. Coward, 70 M. J. Cowart, 6 D. C. Coyne, 1 R. Coyne, 127 J. D. E. Creighton, 7 T. D. Creighton, 128 A. W. Criswell, 47 M. Croquette, 86 S. G. Crowder, 129 J. R. Cudell, 46 T. J. Cullen, 2 A. Cumming, 53 R. Cummings, 53 L. Cunningham, 53 E. Cuoco, 33, 130, 18 M. Curyglyph[suppress]lo, 87 P. Dabadie, 22 T. Dal Canton, 32 S. Dall'Osso, 25 G. D'alya, 131 A. Dana, 57 L. M. DaneshgaranBajastani, 68 B. D'Angelo, 97, 69 B. Danila, 132 S. Danilishin, 133, 40 S. D'Antonio, 104 K. Danzmann, 9, 10 C. Darsow-Fromm, 108 A. Dasgupta, 64 L. E. H. Datrier, 53 S. Datta, 11 V. Dattilo, 33 I. Dave, 71 M. Davier, 32 G. S. Davies, 134 D. Davis, 1 M. C. Davis, 106 E. J. Daw, 135 R. Dean, 106 D. DeBra, 57 M. Deenadayalan, 11 J. Degallaix, 136 M. De Laurentis, 21, 4 S. Del'eglise, 86 V. Del Favero, 109 F. De Lillo, 39 N. De Lillo, 53 W. Del Pozzo, 58, 18 L. M. DeMarchi, 15 F. De Matteis, 103, 104 V. D'Emilio, 17 N. Demos, 54 T. Dent, 92 A. Depasse, 39 R. De Pietri, 137, 138 R. De Rosa, 21, 4 C. De Rossi, 33 R. DeSalvo, 105 R. De Simone, 114 S. Dhurandhar, 11 M. C. D'ıaz, 128 M. Diaz-Ortiz Jr., 56 N. A. Didio, 45 T. Dietrich, 89, 40 L. Di Fiore, 4 C. Di Fronzo, 14 C. Di Giorgio, 80, 81 F. Di Giovanni, 107 M. Di Giovanni, 25  \nT. Di Girolamo, 21, 4 A. Di Lieto, 58, 18 B. Ding, 123 S. Di Pace, 82, 38 I. Di Palma, 82, 38 F. Di Renzo, 58, 18 A. K. Divakarla, 56 Divyajyoti, 139 A. Dmitriev, 14 Z. Doctor, 44 L. D'Onofrio, 21, 4 F. Donovan, 54 K. L. Dooley, 17 S. Doravari, 11 I. Dorrington, 17 M. Drago, 82, 38 J. C. Driggers, 51 Y. Drori, 1 J.-G. Ducoin, 32 P. Dupej, 53 O. Durante, 80, 81 D. D'Urso, 101, 102 P.-A. Duverne, 32 S. E. Dwyer, 51 C. Eassa, 51 P. J. Easter, 5 M. Ebersold, 140 T. Eckhardt, 108 G. Eddolls, 53 B. Edelman, 44 T. B. Edo, 1 O. Edy, 134 A. Effler, 6 J. Eichholz, 8 S. S. Eikenberry, 56 M. Eisenmann, 24 R. A. Eisenstein, 54 A. Ejlli, 17 E. Engelby, 31 L. Errico, 21, 4 R. C. Essick, 141 H. Estell'es, 122 D. Estevez, 142 Z. Etienne, 143 T. Etzel, 1 M. Evans, 54 T. M. Evans, 6 B. E. Ewing, 126 V. Fafone, 103, 104, 25 H. Fair, 45 S. Fairhurst, 17 S. P. Fanning, 7 A. M. Farah, 141 S. Farinon, 69 B. Farr, 44 W. M. Farr, 94, 95 N. W. Farrow, 5 E. J. Fauchon-Jones, 17 G. Favaro, 61 M. Favata, 144 M. Fays, 46 M. Fazio, 145 J. Feicht, 1 M. M. Fejer, 57 E. Fenyvesi, 55, 146 D. L. Ferguson, 147 A. Fernandez-Galiana, 54 I. Ferrante, 58, 18 T. A. Ferreira, 16 F. Fidecaro, 58, 18 P. Figura, 87 I. Fiori, 33 M. Fishbach, 15 R. P. Fisher, 43 R. Fittipaldi, 148, 81 V. Fiumara, 149, 81 R. Flaminio, 24, 150 E. Floden, 47 H. Fong, 99 J. A. Font, 107, 151 B. Fornal, 152 P. W. F. Forsyth, 8 A. Franke, 108 S. Frasca, 82, 38 F. Frasconi, 18 C. Frederick, 153 J. P. Freed, 29 Z. Frei, 131 A. Freise, 154 R. Frey, 44 P. Fritschel, 54 V. V. Frolov, 6 G. G. Fronz'e, 20 P. Fulda, 56 M. Fyffe, 6 H. A. Gabbard, 53 W. Gabella, 155 B. U. Gadre, 89 J. R. Gair, 89 J. Gais, 93 S. Galaudage, 5 R. Gamba, 13 D. Ganapathy, 54 A. Ganguly, 19 S. G. Gaonkar, 11 B. Garaventa, 69, 97 F. Garc'ıa, 30 C. Garc'ıa-N'u˜nez, 77 C. Garc'ıa-Quir'os, 122 F. Garufi, 21, 4 B. Gateley, 51 S. Gaudio, 29 V. Gayathri, 56 G. Gemme, 69 A. Gennai, 18 J. George, 71 R. N. George, 147 O. Gerberding, 108 L. Gergely, 132 P. Gewecke, 108 S. Ghonge, 91 Abhirup Ghosh, 89 Archisman Ghosh, 156 Shaon Ghosh, 7, 144 Shrobana Ghosh, 17 B. Giacomazzo, 48, 49, 50 L. Giacoppo, 82, 38 J. A. Giaime, 2, 6 K. D. Giardina, 6 D. R. Gibson, 77 C. Gier, 26 M. Giesler, 157 P. Giri, 18, 58 F. Gissi, 66 J. Glanzer, 2 A. E. Gleckl, 31 P. Godwin, 126 E. Goetz, 158 R. Goetz, 56 N. Gohlke, 9, 10 B. Goncharov, 5, 25 G. Gonz'alez, 2 A. Gopakumar, 159 M. Gosselin, 33 R. Gouaty, 24 D. W. Gould, 8 B. Grace, 8 A. Grado, 160, 4 M. Granata, 136 V. Granata, 80 A. Grant, 53 S. Gras, 54 P. Grassia, 1 C. Gray, 51 R. Gray, 53 G. Greco, 59 A. C. Green, 56 R. Green, 17 A. M. Gretarsson, 29 E. M. Gretarsson, 29 D. Griffith, 1 W. Griffiths, 17 H. L. Griggs, 91 G. Grignani, 60, 59 A. Grimaldi, 75, 76 S. J. Grimm, 25, 85 H. Grote, 17 S. Grunewald, 89 P. Gruning, 32 D. Guerra, 107 G. M. Guidi, 36, 37 A. R. Guimaraes, 2 G. Guix'e, 23 H. K. Gulati, 64 H.-K. Guo, 152 Y. Guo, 40 Anchal Gupta, 1 Anuradha Gupta, 161 P. Gupta, 40, 98 E. K. Gustafson, 1 R. Gustafson, 162 F. Guzman, 163 L. Haegel, 30 O. Halim, 28, 164 E. D. Hall, 54 E. Z. Hamilton, 140 G. Hammond, 53 M. Haney, 140 J. Hanks, 51 C. Hanna, 126 M. D. Hannam, 17 O. Hannuksela, 98, 40 H. Hansen, 51 T. J. Hansen, 29 J. Hanson, 6 T. Harder, 79 T. Hardwick, 2 K. Haris, 40, 98 J. Harms, 25, 85 G. M. Harry, 165 I. W. Harry, 134 D. Hartwig, 108 B. Haskell, 65 R. K. Hasskew, 6 C.-J. Haster, 54 K. Haughian, 53 F. J. Hayes, 53 J. Healy, 109 A. Heidmann, 86 A. Heidt, 9, 10 M. C. Heintze, 6 J. Heinze, 9, 10 J. Heinzel, 166 H. Heitmann, 79 F. Hellman, 167 P. Hello, 32 A. F. Helmling-Cornell, 44 G. Hemming, 33 M. Hendry, 53 I. S. Heng, 53 E. Hennes, 40 J. Hennig, 168 M. H. Hennig, 168 A. G. Hernandez, 68 F. Hernandez Vivanco, 5 M. Heurs, 9, 10 S. Hild, 133, 40 P. Hill, 26 A. S. Hines, 163 S. Hochheim, 9, 10 D. Hofman, 136 J. N. Hohmann, 108 D. G. Holcomb, 106 N. A. Holland, 8 K. Holley-Bockelmann, 155 I. J. Hollows, 135 Z. J. Holmes, 67 K. Holt, 6 D. E. Holz, 141 P. Hopkins, 17 J. Hough, 53 S. Hourihane, 113 E. J. Howell, 70 C. G. Hoy, 17 D. Hoyland, 14 A. Hreibi, 9, 10 Y. Hsu, 110 Y. Huang, 54 M. T. Hubner, 5 A. D. Huddart, 120 B. Hughey, 29 V. Hui, 24 S. Husa, 122 S. H. Huttner, 53 R. Huxford, 126 T. Huynh-Dinh, 6 B. Idzkowski, 87 A. Iess, 103, 104 C. Ingram, 67 M. Isi, 54 K. Isleif, 108 B. R. Iyer, 19 V. JaberianHamedan, 70 T. Jacqmin, 86 S. J. Jadhav, 169 S. P. Jadhav, 11 A. L. James, 17 A. Z. Jan, 109 K. Jani, 155 J. Janquart, 98, 40 K. Janssens, 170, 79 N. N. Janthalur, 169 P. Jaranowski, 171 D. Jariwala, 56 R. Jaume, 122 A. C. Jenkins, 41 K. Jenner, 67 M. Jeunon, 47 W. Jia, 54 G. R. Johns, 43 N. K. Johnson-McDaniel, 12 A. W. Jones, 70 D. I. Jones, 172 J. D. Jones, 51 P. Jones, 14 R. Jones, 53 R. J. G. Jonker, 40 L. Ju, 70 J. Junker, 9, 10 V. Juste, 142 C. V. Kalaghatgi, 17, 98 V. Kalogera, 15 B. Kamai, 1 S. Kandhasamy, 11 G. Kang, 173 J. B. Kanner, 1 Y. Kao, 110 S. J. Kapadia, 19 D. P. Kapasi, 8 S. Karat, 1 C. Karathanasis, 174 S. Karki, 73 R. Kashyap, 126 M. Kasprzack, 1 W. Kastaun, 9, 10 S. Katsanevas, 33 E. Katsavounidis, 54 W. Katzman, 6 T. Kaur, 70 K. Kawabe, 51 F. K'ef'elian, 79 D. Keitel, 122 J. S. Key, 175 S. Khadka, 57 F. Y. Khalili, 74 S. Khan, 17 E. A. Khazanov, 176 N. Khetan, 25, 85 M. Khursheed, 71 N. Kijbunchoo, 8 C. Kim, 177 J. C. Kim, 178 K. Kim, 179 W. S. Kim, 180 Y.-M. Kim, 181 C. Kimball, 15 M. Kinley-Hanlon, 53 R. Kirchhoff, 9, 10 J. S. Kissel, 51 L. Kleybolte, 108 S. Klimenko, 56 A. M. Knee, 158 T. D. Knowles, 143 E. Knyazev, 54 P. Koch, 9, 10 G. Koekoek, 40, 133 S. Koley, 25 P. Kolitsidou, 17 M. Kolstein, 174 K. Komori, 54 V. Kondrashov, 1 A. Kontos, 182 N. Koper, 9, 10 M. Korobko, 108 M. Kovalam, 70 D. B. Kozak, 1 V. Kringel, 9, 10 N. V. Krishnendu, 9, 10 A. Kr'olak, 183, 184 G. Kuehn, 9, 10 F. Kuei, 110 P. Kuijer, 40 S. ˜ Kulkarni, 161 A. Kumar, 169 P. Kumar, 157 Rahul Kumar, 51 Rakesh Kumar, 64 K. Kuns, 54 S. Kuwahara, 99 P. Lagabbe, 24 D. Laghi, 58, 18 E. Lalande, 185 T. L. Lam, 93 A. Lamberts, 79, 186 M. Landry, 51 B. B. Lane, 54 R. N. Lang, 54 J. Lange, 147 B. Lantz, 57 I. La Rosa, 24 A. Lartaux-Vollard, 32 P. D. Lasky, 5 M. Laxen, 6 A. Lazzarini, 1 C. Lazzaro, 61, 62 P. Leaci, 82, 38 S. Leavey, 9, 10 Y. K. Lecoeuche, 158  \nH. M. Lee, 116 H. W. Lee, 178 J. Lee, 116 K. Lee, 187 J. Lehmann, 9, 10 A. Lemaˆıtre, 188 N. Leroy, 32 N. Letendre, 24 C. Levesque, 185 Y. Levin, 5 J. N. Leviton, 162 K. Leyde, 30 A. K. Y. Li, 1 B. Li, 110 J. Li, 15 T. G. F. Li, 93 X. Li, 113 F. Linde, 189, 40 S. D. Linker, 68 J. N. Linley, 53 T. B. Littenberg, 190 J. Liu, 9, 10 K. Liu, 110 X. Liu, 7 F. Llamas, 128 M. Llorens-Monteagudo, 107 R. K. L. Lo, 1 A. Lockwood, 191 L. T. London, 54 A. Longo, 192, 193 D. Lopez, 140 M. Lopez Portilla, 98 M. Lorenzini, 103, 104 V. Loriette, 194 M. Lormand, 6 G. Losurdo, 18 T. P. Lott, 91 J. D. Lough, 9, 10 C. O. Lousto, 109 G. Lovelace, 31 J. F. Lucaccioni, 153 H. Luck, 9, 10 D. Lumaca, 103, 104 A. P. Lundgren, 134 J. E. Lynam, 43 R. Macas, 134 M. MacInnis, 54 D. M. Macleod, 17 I. A. O. MacMillan, 1 A. Macquet, 79 I. Maga˜na Hernandez, 7 C. Magazz'u, 18 R. M. Magee, 1 R. Maggiore, 14 M. Magnozzi, 69, 97 S. Mahesh, 143 E. Majorana, 82, 38 C. Makarem, 1 I. Maksimovic, 194 S. Maliakal, 1 A. Malik, 71 N. Man, 79 V. Mandic, 47 V. Mangano, 82, 38 J. L. Mango, 195 G. L. Mansell, 51, 54 M. Manske, 7 M. Mantovani, 33 M. Mapelli, 61, 62 F. Marchesoni, 196, 59, 197 F. Marion, 24 Z. Mark, 113 S. M'arka, 35 Z. M'arka, 35 C. Markakis, 12 A. S. Markosyan, 57 A. Markowitz, 1 E. Maros, 1 A. Marquina, 124 S. Marsat, 30 F. Martelli, 36, 37 I. W. Martin, 53 R. M. Martin, 144 M. Martinez, 174 V. A. Martinez, 56 V. Martinez, 22 K. Martinovic, 41 D. V. Martynov, 14 E. J. Marx, 54 H. Masalehdan, 108 K. Mason, 54 E. Massera, 135 A. Masserot, 24 T. J. Massinger, 54 M. Masso-Reid, 53 S. Mastrogiovanni, 30 A. Matas, 89 M. Mateu-Lucena, 122 F. Matichard, 1, 54 M. Matiushechkina, 9, 10 N. Mavalvala, 54 J. J. McCann, 70 R. McCarthy, 51 D. E. McClelland, 8 P. K. McClincy, 126 S. McCormick, 6 L. McCuller, 54 G. I. McGhee, 53 S. C. McGuire, 198 C. McIsaac, 134 J. McIver, 158 T. McRae, 8 S. T. McWilliams, 143 D. Meacher, 7 M. Mehmet, 9, 10 A. K. Mehta, 89 Q. Meijer, 98 A. Melatos, 100 D. A. Melchor, 31 G. Mendell, 51 A. Menendez-Vazquez, 174 C. S. Menoni, 145 R. A. Mercer, 7 L. Mereni, 136 K. Merfeld, 44 E. L. Merilh, 6 J. D. Merritt, 44 M. Merzougui, 79 S. Meshkov, 1, a C. Messenger, 53 C. Messick, 147 P. M. Meyers, 100 F. Meylahn, 9, 10 A. Mhaske, 11 A. Miani, 75, 76 H. Miao, 14 I. Michaloliakos, 56 C. Michel, 136 H. Middleton, 100 L. Milano, 21 A. Miller, 68 A. L. Miller, 39 B. Miller, 72, 40 M. Millhouse, 100 J. C. Mills, 17 E. Milotti, 164, 28 O. Minazzoli, 79, 199 Y. Minenkov, 104 Ll. M. Mir, 174 M. Miravet-Ten'es, 107 C. Mishra, 139 T. Mishra, 56 T. Mistry, 135 S. Mitra, 11 V. P. Mitrofanov, 74 G. Mitselmakher, 56 R. Mittleman, 54 Geoffrey Mo, 54 E. Moguel, 153 K. Mogushi, 73 S. R. P. Mohapatra, 54 S. R. Mohite, 7 I. Molina, 31 M. Molina-Ruiz, 167 M. Mondin, 68 M. Montani, 36, 37 C. J. Moore, 14 D. Moraru, 51 F. Morawski, 65 A. More, 11 C. Moreno, 29 G. Moreno, 51 S. Morisaki, 7 B. Mours, 142 C. M. Mow-Lowry, 14, 154 S. Mozzon, 134 F. Muciaccia, 82, 38 Arunava Mukherjee, 200 D. Mukherjee, 126 Soma Mukherjee, 128 Subroto Mukherjee, 64 Suvodip Mukherjee, 72 N. Mukund, 9, 10 A. Mullavey, 6 J. Munch, 67 E. A. Mu˜niz, 45 P. G. Murray, 53 R. Musenich, 69, 97 S. Muusse, 67 S. L. Nadji, 9, 10 A. Nagar, 20, 201 V. Napolano, 33 I. Nardecchia, 103, 104 L. Naticchioni, 38 B. Nayak, 68 R. K. Nayak, 202 B. F. Neil, 70 J. Neilson, 66, 81 G. Nelemans, 203 T. J. N. Nelson, 6 M. Nery, 9, 10 P. Neubauer, 153 A. Neunzert, 175 K. Y. Ng, 54 S. W. S. Ng, 67 C. Nguyen, 30 P. Nguyen, 44 T. Nguyen, 54 S. A. Nichols, 2 S. Nissanke, 72, 40 E. Nitoglia, 115 F. Nocera, 33 M. Norman, 17 C. North, 17 L. K. Nuttall, 134 J. Oberling, 51 B. D. O'Brien, 56 J. O'Dell, 120 E. Oelker, 53 G. Oganesyan, 25, 85 J. J. Oh, 180 S. H. Oh, 180 F. Ohme, 9, 10 H. Ohta, 99 M. A. Okada, 16 C. Olivetto, 33 R. Oram, 6 B. O'Reilly, 6 R. G. Ormiston, 47 N. D. Ormsby, 43 L. F. Ortega, 56 R. O'Shaughnessy, 109 E. O'Shea, 157 S. Ossokine, 89 C. Osthelder, 1 D. J. Ottaway, 67 H. Overmier, 6 A. E. Pace, 126 G. Pagano, 58, 18 M. A. Page, 70 G. Pagliaroli, 25, 85 A. Pai, 84 S. A. Pai, 71 J. R. Palamos, 44 O. Palashov, 176 C. Palomba, 38 H. Pan, 110 P. K. Panda, 169 P. T. H. Pang, 40, 98 C. Pankow, 15 F. Pannarale, 82, 38 B. C. Pant, 71 F. H. Panther, 70 F. Paoletti, 18 A. Paoli, 33 A. Paolone, 38, 204 H. Park, 7 W. Parker, 6, 198 D. Pascucci, 40 A. Pasqualetti, 33 R. Passaquieti, 58, 18 D. Passuello, 18 M. Patel, 43 M. Pathak, 67 B. Patricelli, 33, 18 A. S. Patron, 2 S. Patrone, 82, 38 S. Paul, 44 E. Payne, 5 M. Pedraza, 1 M. Pegoraro, 62 A. Pele, 6 S. Penn, 205 A. Perego, 75, 76 A. Pereira, 22 T. Pereira, 206 C. J. Perez, 51 C. P'erigois, 24 C. C. Perkins, 56 A. Perreca, 75, 76 S. Perri'es, 115 J. Petermann, 108 D. Petterson, 1 H. P. Pfeiffer, 89 K. A. Pham, 47 K. S. Phukon, 40, 189 O. J. Piccinni, 38 M. Pichot, 79 M. Piendibene, 58, 18 F. Piergiovanni, 36, 37 L. Pierini, 82, 38 V. Pierro, 66, 81 G. Pillant, 33 M. Pillas, 32 F. Pilo, 18 L. Pinard, 136 I. M. Pinto, 66, 81, 207 M. Pinto, 33 K. Piotrzkowski, 39 M. Pirello, 51 M. D. Pitkin, 208 E. Placidi, 82, 38 L. Planas, 122 W. Plastino, 192, 193 C. Pluchar, 119 R. Poggiani, 58, 18 E. Polini, 24 D. Y. T. Pong, 93 S. Ponrathnam, 11 P. Popolizio, 33 E. K. Porter, 30 R. Poulton, 33 J. Powell, 112 M. Pracchia, 24 T. Pradier, 142 A. K. Prajapati, 64 K. Prasai, 57 R. Prasanna, 169 G. Pratten, 14 M. Principe, 66, 207, 81 G. A. Prodi, 209, 76 L. Prokhorov, 14 P. Prosposito, 103, 104 L. Prudenzi, 89 A. Puecher, 40, 98 M. Punturo, 59 F. Puosi, 18, 58 P. Puppo, 38 M. Purrer, 89 H. Qi, 17 V. Quetschke, 128 R. Quitzow-James, 73 F. J. Raab, 51 G. Raaijmakers, 72, 40 H. Radkins, 51 N. Radulesco, 79 P. Raffai, 131 S. X. Rail, 185 S. Raja, 71 C. Rajan, 71 K. E. Ramirez, 6 T. D. Ramirez, 31 A. Ramos-Buades, 89 J. Rana, 126 P. Rapagnani, 82, 38 U. D. Rapol, 210 A. Ray, 7 V. Raymond, 17 N. Raza, 158 M. Razzano, 58, 18 J. Read, 31 L. A. Rees, 165 T. Regimbau, 24 L. Rei, 69 S. Reid, 26 S. W. Reid, 43 D. H. Reitze, 1, 56 P. Relton, 17 A. Renzini, 1 P. Rettegno, 211, 20 A. Reza, 40 M. Rezac, 31 F. Ricci, 82, 38 D. Richards, 120 J. W. Richardson, 1 L. Richardson, 163 G. Riemenschneider, 211, 20 K. Riles, 162 S. Rinaldi, 18, 58 K. Rink, 158 M. Rizzo, 15 N. A. Robertson, 1, 53 R. Robie, 1  \nF. Robinet, 32 A. Rocchi, 104 S. Rodriguez, 31 L. Rolland, 24 J. G. Rollins, 1 M. Romanelli, 83 R. Romano, 3, 4 C. L. Romel, 51 A. Romero-Rodr'ıguez, 174 I. M. Romero-Shaw, 5 J. H. Romie, 6 S. Ronchini, 25, 85 L. Rosa, 4, 21 C. A. Rose, 7 M. J. B. Rosell, 147 D. Rosi'nska, 87 M. P. Ross, 191 S. Rowan, 53 S. J. Rowlinson, 14 S. Roy, 98 Santosh Roy, 11 Soumen Roy, 212 D. Rozza, 101, 102 P. Ruggi, 33 K. Ruiz-Rocha, 155 K. Ryan, 51 S. Sachdev, 126 T. Sadecki, 51 J. Sadiq, 92 M. Sakellariadou, 41 O. S. Salafia, 50, 49, 48 L. Salconi, 33 M. Saleem, 47 F. Salemi, 75, 76 A. Samajdar, 40, 98 E. J. Sanchez, 1 J. H. Sanchez, 31 L. E. Sanchez, 1 N. Sanchis-Gual, 213 J. R. Sanders, 214 A. Sanuy, 23 T. R. Saravanan, 11 N. Sarin, 5 B. Sassolas, 136 H. Satari, 70 O. Sauter, 56 R. L. Savage, 51 D. Sawant, 84 H. L. Sawant, 11 S. Sayah, 136 D. Schaetzl, 1 M. Scheel, 113 J. Scheuer, 15 M. Schiworski, 67 P. Schmidt, 14 S. Schmidt, 98 R. Schnabel, 108 M. Schneewind, 9, 10 R. M. S. Schofield, 44 A. Schonbeck, 108 B. W. Schulte, 9, 10 B. F. Schutz, 17, 9, 10 E. Schwartz, 17 J. Scott, 53 S. M. Scott, 8 M. Seglar-Arroyo, 24 D. Sellers, 6 A. S. Sengupta, 212 D. Sentenac, 33 E. G. Seo, 93 V. Sequino, 21, 4 A. Sergeev, 176 Y. Setyawati, 98 T. Shaffer, 51 M. S. Shahriar, 15 B. Shams, 152 A. Sharma, 25, 85 P. Sharma, 71 P. Shawhan, 88 N. S. Shcheblanov, 188 M. Shikauchi, 99 D. H. Shoemaker, 54 D. M. Shoemaker, 147 S. ShyamSundar, 71 M. Sieniawska, 87 D. Sigg, 51 L. P. Singer, 96 D. Singh, 126 N. Singh, 87 A. Singha, 133, 40 A. M. Sintes, 122 V. Sipala, 101, 102 V. Skliris, 17 B. J. J. Slagmolen, 8 T. J. Slaven-Blair, 70 J. Smetana, 14 J. R. Smith, 31 R. J. E. Smith, 5 J. Soldateschi, 215, 216, 37 S. N. Somala, 217 E. J. Son, 180 K. Soni, 11 S. Soni, 2 V. Sordini, 115 F. Sorrentino, 69 N. Sorrentino, 58, 18 R. Soulard, 79 T. Souradeep, 210, 11 E. Sowell, 125 V. Spagnuolo, 133, 40 A. P. Spencer, 53 M. Spera, 61, 62 R. Srinivasan, 79 A. K. Srivastava, 64 V. Srivastava, 45 K. Staats, 15 C. Stachie, 79 D. A. Steer, 30 J. Steinhoff, 89 J. Steinlechner, 133, 40 S. Steinlechner, 133, 40 S. Stevenson, 112 D. J. Stops, 14 M. Stover, 153 K. A. Strain, 53 L. C. Strang, 100 G. Stratta, 218, 37 A. Strunk, 51 R. Sturani, 206 A. L. Stuver, 106 S. Sudhagar, 11 V. Sudhir, 54 H. G. Suh, 7 T. Z. Summerscales, 219 H. Sun, 70 L. Sun, 8 S. Sunil, 64 A. Sur, 65 J. Suresh, 99 P. J. Sutton, 17 B. L. Swinkels, 40 M. J. Szczepa'nczyk, 56 P. Szewczyk, 87 M. Tacca, 40 S. C. Tait, 53 C. J. Talbot, 26 C. Talbot, 1 A. J. Tanasijczuk, 39 D. B. Tanner, 56 D. Tao, 1 L. Tao, 56 E. N. Tapia San Mart'ın, 40 C. Taranto, 103 J. D. Tasson, 166 R. Tenorio, 122 J. E. Terhune, 106 L. Terkowski, 108 M. P. Thirugnanasambandam, 11 L. Thomas, 14 M. Thomas, 6 P. Thomas, 51 J. E. Thompson, 17 S. R. Thondapu, 71 K. A. Thorne, 6 E. Thrane, 5 Shubhanshu Tiwari, 140 Srishti Tiwari, 11 V. Tiwari, 17 A. M. Toivonen, 47 K. Toland, 53 A. E. Tolley, 134 M. Tonelli, 58, 18 A. Torres-Forn'e, 107 C. I. Torrie, 1 I. Tosta e Melo, 101, 102 D. Toyra, 8 A. Trapananti, 196, 59 F. Travasso, 59, 196 G. Traylor, 6 M. Trevor, 88 M. C. Tringali, 33 A. Tripathee, 162 L. Troiano, 220, 81 A. Trovato, 30 L. Trozzo, 4 R. J. Trudeau, 1 D. S. Tsai, 110 D. Tsai, 110 K. W. Tsang, 40, 221, 98 M. Tse, 54 R. Tso, 113 L. Tsukada, 99 D. Tsuna, 99 T. Tsutsui, 99 K. Turbang, 222, 170 M. Turconi, 79 A. S. Ubhi, 14 R. P. Udall, 1 K. Ueno, 99 C. S. Unnikrishnan, 159 A. L. Urban, 2 A. Utina, 133, 40 H. Vahlbruch, 9, 10 G. Vajente, 1 A. Vajpeyi, 5 G. Valdes, 163 M. Valentini, 75, 76 V. Valsan, 7 N. van Bakel, 40 M. van Beuzekom, 40 J. F. J. van den Brand, 133, 223, 40 C. Van Den Broeck, 98, 40 D. C. Vander-Hyde, 45 L. van der Schaaf, 40 J. V. van Heijningen, 39 J. Vanosky, 1 N. van Remortel, 170 M. Vardaro, 189, 40 A. F. Vargas, 100 V. Varma, 157 M. Vas'uth, 55 A. Vecchio, 14 G. Vedovato, 62 J. Veitch, 53 P. J. Veitch, 67 J. Venneberg, 9, 10 G. Venugopalan, 1 D. Verkindt, 24 P. Verma, 184 Y. Verma, 71 D. Veske, 35 F. Vetrano, 36 A. Vicer'e, 36, 37 S. Vidyant, 45 A. D. Viets, 195 A. Vijaykumar, 19 V. Villa-Ortega, 92 J.-Y. Vinet, 79 A. Virtuoso, 164, 28 S. Vitale, 54 T. Vo, 45 H. Vocca, 60, 59 E. R. G. von Reis, 51 J. S. A. von Wrangel, 9, 10 C. Vorvick, 51 S. P. Vyatchanin, 74 L. E. Wade, 153 M. Wade, 153 K. J. Wagner, 109 R. C. Walet, 40 M. Walker, 43 G. S. Wallace, 26 L. Wallace, 1 S. Walsh, 7 J. Z. Wang, 162 W. H. Wang, 128 R. L. Ward, 8 J. Warner, 51 M. Was, 24 N. Y. Washington, 1 J. Watchi, 123 B. Weaver, 51 S. A. Webster, 53 M. Weinert, 9, 10 A. J. Weinstein, 1 R. Weiss, 54 C. M. Weller, 191 R. Weller, 155 F. Wellmann, 9, 10 L. Wen, 70 P. Weßels, 9, 10 K. Wette, 8 J. T. Whelan, 109 D. D. White, 31 B. F. Whiting, 56 C. Whittle, 54 D. Wilken, 9, 10 D. Williams, 53 M. J. Williams, 53 A. R. Williamson, 134 J. L. Willis, 1 B. Willke, 9, 10 D. J. Wilson, 119 W. Winkler, 9, 10 C. C. Wipf, 1 T. Wlodarczyk, 89 G. Woan, 53 J. Woehler, 9, 10 J. K. Wofford, 109 I. C. F. Wong, 93 D. S. Wu, 9, 10 D. M. Wysocki, 7 L. Xiao, 1 H. Yamamoto, 1 F. W. Yang, 152 L. Yang, 145 Yang Yang, 56 Z. Yang, 47 M. J. Yap, 8 D. W. Yeeles, 17 A. B. Yelikar, 109 M. Ying, 110 J. Yoo, 157 Hang Yu, 113 Haocun Yu, 54 A. Zadro˙zny, 184 M. Zanolin, 29 T. Zelenova, 33 J.-P. Zendri, 62 M. Zevin, 141 J. Zhang, 70 L. Zhang, 1 T. Zhang, 14 Y. Zhang, 163 C. Zhao, 70 G. Zhao, 123 Yue Zhao, 152 R. Zhou, 167 Z. Zhou, 15 X. J. Zhu, 5 A. B. Zimmerman, 147 Y. Zlochower, 109 M. E. Zucker, 1, 54 and J. Zweizig 1  \n(The LIGO Scientific Collaboration and the Virgo Collaboration)  \n1  \nLIGO Laboratory, California Institute of Technology, Pasadena, CA 91125, USA 2 Louisiana State University, Baton Rouge, LA 70803, USA 3 Dipartimento di Farmacia, Universit'a di Salerno, I-84084 Fisciano, Salerno, Italy 4 INFN, Sezione di Napoli, Complesso Universitario di Monte S. Angelo, I-80126 Napoli, Italy 5 OzGrav, School of Physics & Astronomy, Monash University, Clayton 3800, Victoria, Australia 6 LIGO Livingston Observatory, Livingston, LA 70754, USA  \n19  \n7  \nUniversity of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA 8 OzGrav, Australian National University, Canberra, Australian Capital Territory 0200, Australia 9 Max Planck Institute for Gravitational Physics (Albert Einstein Institute), D-30167 Hannover, Germany 10 Leibniz Universitat Hannover, D-30167 Hannover, Germany 11 Inter-University Centre for Astronomy and Astrophysics, Pune 411007, India 12 University of Cambridge, Cambridge CB2 1TN, United Kingdom 13 Theoretisch-Physikalisches Institut, Friedrich-Schiller-Universitat Jena, D-07743 Jena, Germany 14 University of Birmingham, Birmingham B15 2TT, United Kingdom 15 Center for Interdisciplinary Exploration & Research in Astrophysics (CIERA), Northwestern University, Evanston, IL 60208, USA 16 Instituto Nacional de Pesquisas Espaciais, 12227-010 S˜ao Jos'e dos Campos, S˜ao Paulo, Brazil 17 Gravity Exploration Institute, Cardiff University, Cardiff CF24 3AA, United Kingdom 18 INFN, Sezione di Pisa, I-56127 Pisa, Italy International Centre for Theoretical Sciences, Tata Institute of Fundamental Research, Bengaluru 560089, India 20 INFN Sezione di Torino, I-10125 Torino, Italy 21 Universit'a di Napoli 'Federico II', Complesso Universitario di Monte S. Angelo, I-80126 Napoli, Italy 22 Universit'e de Lyon, Universit'e Claude Bernard Lyon 1, CNRS, Institut Lumi'ere Mati'ere, F-69622 Villeurbanne, France 23 Institut de Ci'encies del Cosmos (ICCUB), Universitat de Barcelona, C/ Mart'ı i Franqu'es 1, Barcelona, 08028, Spain 24 Laboratoire d'Annecy de Physique des Particules (LAPP), Univ. Grenoble Alpes, Universit'e Savoie Mont Blanc, CNRS/IN2P3, F-74941 Annecy, France 25 Gran Sasso Science Institute (GSSI), I-67100 L'Aquila, Italy 26 SUPA, University of Strathclyde, Glasgow G1 1XQ, United Kingdom 27 Dipartimento di Scienze Matematiche, Informatiche e Fisiche, Universit'a di Udine, I-33100 Udine, Italy 28 INFN, Sezione di Trieste, I-34127 Trieste, Italy 29 Embry-Riddle Aeronautical University, Prescott, AZ 86301, USA 30 Universit'e de Paris, CNRS, Astroparticule et Cosmologie, F-75006 Paris, France 31 California State University Fullerton, Fullerton, CA 92831, USA 32 Universit'e Paris-Saclay, CNRS/IN2P3, IJCLab, 91405 Orsay, France 33 European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy 34 Chennai Mathematical Institute, Chennai 603103, India 35 Columbia University, New York, NY 10027, USA 36 Universit'a degli Studi di Urbino 'Carlo Bo', I-61029 Urbino, Italy 37 INFN, Sezione di Firenze, I-50019 Sesto Fiorentino, Firenze, Italy 38 INFN, Sezione di Roma, I-00185 Roma, Italy 39 Universit'e catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium 40 Nikhef, Science Park 105, 1098 XG Amsterdam, Netherlands 41 King's College London, University of London, London WC2R 2LS, United Kingdom 42 Korea Institute of Science and Technology Information, Daejeon 34141, South Korea 43 Christopher Newport University, Newport News, VA 23606, USA 44 University of Oregon, Eugene, OR 97403, USA 45 Syracuse University, Syracuse, NY 13244, USA 46 Universit'e de Li'ege, B-4000 Li'ege, Belgium 47 University of Minnesota, Minneapolis, MN 55455, USA 48 Universit'a degli Studi di Milano-Bicocca, I-20126 Milano, Italy 49 INFN, Sezione di Milano-Bicocca, I-20126 Milano, Italy 50 INAF, Osservatorio Astronomico di Brera sede di Merate, I-23807 Merate, Lecco, Italy 51 LIGO Hanford Observatory, Richland, WA 99352, USA 52 Dipartimento di Medicina, Chirurgia e Odontoiatria 'Scuola Medica Salernitana', Universit'a di Salerno, I-84081 Baronissi, Salerno, Italy 53 SUPA, University of Glasgow, Glasgow G12 8QQ, United Kingdom 54 LIGO Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 55 Wigner RCP, RMKI, H-1121 Budapest, Konkoly Thege Mikl'os 'ut 29-33, Hungary 56 University of Florida, Gainesville, FL 32611, USA 57 Stanford University, Stanford, CA 94305, USA 58 Universit'a di Pisa, I-56127 Pisa, Italy 59 INFN, Sezione di Perugia, I-06123 Perugia, Italy 60 Universit'a di Perugia, I-06123 Perugia, Italy 61 Universit'a di Padova, Dipartimento di Fisica e Astronomia, I-35131 Padova, Italy 62 INFN, Sezione di Padova, I-35131 Padova, Italy 63 Montana State University, Bozeman, MT 59717, USA 64 Institute for Plasma Research, Bhat, Gandhinagar 382428, India  \n65 Nicolaus Copernicus Astronomical Center, Polish Academy of Sciences, 00-716, Warsaw, Poland  \n107  \n66 Dipartimento di Ingegneria, Universit'a del Sannio, I-82100 Benevento, Italy 67 OzGrav, University of Adelaide, Adelaide, South Australia 5005, Australia 68 California State University, Los Angeles, 5151 State University Dr, Los Angeles, CA 90032, USA 69 INFN, Sezione di Genova, I-16146 Genova, Italy 70 OzGrav, University of Western Australia, Crawley, Western Australia 6009, Australia 71 RRCAT, Indore, Madhya Pradesh 452013, India 72 GRAPPA, Anton Pannekoek Institute for Astronomy and Institute for High-Energy Physics, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Netherlands 73 Missouri University of Science and Technology, Rolla, MO 65409, USA 74 Faculty of Physics, Lomonosov Moscow State University, Moscow 119991, Russia 75 Universit'a di Trento, Dipartimento di Fisica, I-38123 Povo, Trento, Italy 76 INFN, Trento Institute for Fundamental Physics and Applications, I-38123 Povo, Trento, Italy 77 SUPA, University of the West of Scotland, Paisley PA1 2BE, United Kingdom 78 Bar-Ilan University, Ramat Gan, 5290002, Israel 79 Artemis, Universit'e Cˆote d'Azur, Observatoire de la Cˆote d'Azur, CNRS, F-06304 Nice, France 80 Dipartimento di Fisica 'E.R. Caianiello', Universit'a di Salerno, I-84084 Fisciano, Salerno, Italy 81 INFN, Sezione di Napoli, Gruppo Collegato di Salerno, Complesso Universitario di Monte S. Angelo, I-80126 Napoli, Italy 82 Universit'a di Roma 'La Sapienza', I-00185 Roma, Italy 83 Univ Rennes, CNRS, Institut FOTON - UMR6082, F-3500 Rennes, France 84 Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India 85 INFN, Laboratori Nazionali del Gran Sasso, I-67100 Assergi, Italy 86 Laboratoire Kastler Brossel, Sorbonne Universit'e, CNRS, ENS-Universit'e PSL, Coll'ege de France, F-75005 Paris, France 87 Astronomical Observatory Warsaw University, 00-478 Warsaw, Poland 88 University of Maryland, College Park, MD 20742, USA 89 Max Planck Institute for Gravitational Physics (Albert Einstein Institute), D-14476 Potsdam, Germany 90 L2IT, Laboratoire des 2 Infinis - Toulouse, Universit'e de Toulouse, CNRS/IN2P3, UPS, F-31062 Toulouse Cedex 9, France 91 School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA 92 IGFAE, Campus Sur, Universidade de Santiago de Compostela, 15782 Spain 93 The Chinese University of Hong Kong, Shatin, NT, Hong Kong 94 Stony Brook University, Stony Brook, NY 11794, USA 95 Center for Computational Astrophysics, Flatiron Institute, New York, NY 10010, USA 96 NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA 97 Dipartimento di Fisica, Universit'a degli Studi di Genova, I-16146 Genova, Italy 98 Institute for Gravitational and Subatomic Physics (GRASP), Utrecht University, Princetonplein 1, 3584 CC Utrecht, Netherlands 99 RESCEU, University of Tokyo, Tokyo, 113-0033, Japan. 100 OzGrav, University of Melbourne, Parkville, Victoria 3010, Australia 101 Universit'a degli Studi di Sassari, I-07100 Sassari, Italy 102 INFN, Laboratori Nazionali del Sud, I-95125 Catania, Italy 103 Universit'a di Roma Tor Vergata, I-00133 Roma, Italy 104 INFN, Sezione di Roma Tor Vergata, I-00133 Roma, Italy 105 University of Sannio at Benevento, I-82100 Benevento, Italy and INFN, Sezione di Napoli, I-80100 Napoli, Italy 106 Villanova University, 800 Lancaster Ave, Villanova, PA 19085, USA Departamento de Astronom'ıa y Astrof'ısica, Universitat de Val'encia, E-46100 Burjassot, Val'encia, Spain 108 Universitat Hamburg, D-22761 Hamburg, Germany 109 Rochester Institute of Technology, Rochester, NY 14623, USA 110 National Tsing Hua University, Hsinchu City, 30013 Taiwan, Republic of China 111 OzGrav, Charles Sturt University, Wagga Wagga, New South Wales 2678, Australia 112 OzGrav, Swinburne University of Technology, Hawthorn VIC 3122, Australia 113 CaRT, California Institute of Technology, Pasadena, CA 91125, USA 114 Dipartimento di Ingegneria Industriale (DIIN), Universit'a di Salerno, I-84084 Fisciano, Salerno, Italy 115 Universit'e Lyon, Universit'e Claude Bernard Lyon 1, CNRS, IP2I Lyon / IN2P3, UMR 5822, F-69622 Villeurbanne, France 116 Seoul National University, Seoul 08826, South Korea 117 Pusan National University, Busan 46241, South Korea 118 INAF, Osservatorio Astronomico di Padova, I-35122 Padova, Italy 119 University of Arizona, Tucson, AZ 85721, USA 120 Rutherford Appleton Laboratory, Didcot OX11 0DE, United Kingdom 121  \nUniversit'e libre de Bruxelles, Avenue Franklin Roosevelt 50 - 1050 Bruxelles, Belgium  \n122 Universitat de les Illes Balears, IAC3-IEEC, E-07122 Palma de Mallorca, Spain  \n123 Universit'e Libre de Bruxelles, Brussels 1050, Belgium 124 Departamento de Matem'aticas, Universitat de Val'encia, E-46100 Burjassot, Val'encia, Spain 125 Texas Tech University, Lubbock, TX 79409, USA 126 The Pennsylvania State University, University Park, PA 16802, USA 127 University of Rhode Island, Kingston, RI 02881, USA 128 The University of Texas Rio Grande Valley, Brownsville, TX 78520, USA 129 Bellevue College, Bellevue, WA 98007, USA 130 Scuola Normale Superiore, Piazza dei Cavalieri, 7 - 56126 Pisa, Italy 131 MTA-ELTE Astrophysics Research Group, Institute of Physics, Eotvos University, Budapest 1117, Hungary 132 University of Szeged, D'om t'er 9, Szeged 6720, Hungary 133 Maastricht University, P.O. Box 616, 6200 MD Maastricht, Netherlands 134 University of Portsmouth, Portsmouth, PO1 3FX, United Kingdom 135 The University of Sheffield, Sheffield S10 2TN, United Kingdom 136 Universit'e Lyon, Universit'e Claude Bernard Lyon 1, CNRS, Laboratoire des Mat'eriaux Avanc'es (LMA), IP2I Lyon / IN2P3, UMR 5822, F-69622 Villeurbanne, France 137 Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Universit'a di Parma, I-43124 Parma, Italy 138 INFN, Sezione di Milano Bicocca, Gruppo Collegato di Parma, I-43124 Parma, Italy 139 Indian Institute of Technology Madras, Chennai 600036, India 140 Physik-Institut, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland 141 University of Chicago, Chicago, IL 60637, USA 142 Universit'e de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France 143 West Virginia University, Morgantown, WV 26506, USA 144 Montclair State University, Montclair, NJ 07043, USA 145 Colorado State University, Fort Collins, CO 80523, USA 146 Institute for Nuclear Research, Hungarian Academy of Sciences, Bem t'er 18/c, H-4026 Debrecen, Hungary 147 Department of Physics, University of Texas, Austin, TX 78712, USA 148 CNR-SPIN, c/o Universit'a di Salerno, I-84084 Fisciano, Salerno, Italy 149 Scuola di Ingegneria, Universit'a della Basilicata, I-85100 Potenza, Italy 150 Gravitational Wave Science Project, National Astronomical Observatory of Japan (NAOJ), Mitaka City, Tokyo 181-8588, Japan 151 Observatori Astron'omic, Universitat de Val'encia, E-46980 Paterna, Val'encia, Spain 152 The University of Utah, Salt Lake City, UT 84112, USA 153 Kenyon College, Gambier, OH 43022, USA 154 Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, Netherlands 155 Vanderbilt University, Nashville, TN 37235, USA 156 Universiteit Gent, B-9000 Gent, Belgium 157 Cornell University, Ithaca, NY 14850, USA 158 University of British Columbia, Vancouver, BC V6T 1Z4, Canada 159 Tata Institute of Fundamental Research, Mumbai 400005, India 160 INAF, Osservatorio Astronomico di Capodimonte, I-80131 Napoli, Italy 161 The University of Mississippi, University, MS 38677, USA 162 University of Michigan, Ann Arbor, MI 48109, USA 163 Texas A&M University, College Station, TX 77843, USA 164 Dipartimento di Fisica, Universit'a di Trieste, I-34127 Trieste, Italy 165 American University, Washington, D.C. 20016, USA 166 Carleton College, Northfield, MN 55057, USA 167 University of California, Berkeley, CA 94720, USA 168 Maastricht University, 6200 MD, Maastricht, Netherlands 169 Directorate of Construction, Services & Estate Management, Mumbai 400094, India 170 Universiteit Antwerpen, Prinsstraat 13, 2000 Antwerpen, Belgium 171 University of Biaglyph[suppress]lystok, 15-424 Biaglyph[suppress]lystok, Poland 172 University of Southampton, Southampton SO17 1BJ, United Kingdom 173 Chung-Ang University, Seoul 06974, South Korea 174 Institut de F'ısica d'Altes Energies (IFAE), Barcelona Institute of Science and Technology, and ICREA, E-08193 Barcelona, Spain 175 University of Washington Bothell, Bothell, WA 98011, USA 176 Institute of Applied Physics, Nizhny Novgorod, 603950, Russia 177 Ewha Womans University, Seoul 03760, South Korea 178 Inje University Gimhae, South Gyeongsang 50834, South Korea 179 Korea Astronomy and Space Science Institute, Daejeon 34055, South Korea 180 National Institute for Mathematical Sciences, Daejeon 34047, South Korea 181  \nUlsan National Institute of Science and Technology, Ulsan 44919, South Korea  \n182  \nBard College, 30 Campus Rd, Annandale-On-Hudson, NY 12504, USA 183 Institute of Mathematics, Polish Academy of Sciences, 00656 Warsaw, Poland 184 National Center for Nuclear Research, 05-400 ' Swierk-Otwock, Poland 185 Universit'e de Montr'eal/Polytechnique, Montreal, Quebec H3T 1J4, Canada 186 Laboratoire Lagrange, Universit'e Cˆote d'Azur, Observatoire Cˆote d'Azur, CNRS, F-06304 Nice, France 187 Sungkyunkwan University, Seoul 03063, South Korea 188 NAVIER, ' Ecole des Ponts, Univ Gustave Eiffel, CNRS, Marne-la-Vall'ee, France 189 Institute for High-Energy Physics, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Netherlands 190 NASA Marshall Space Flight Center, Huntsville, AL 35811, USA 191 University of Washington, Seattle, WA 98195, USA 192 Dipartimento di Matematica e Fisica, Universit'a degli Studi Roma Tre, I-00146 Roma, Italy 193 INFN, Sezione di Roma Tre, I-00146 Roma, Italy 194 ESPCI, CNRS, F-75005 Paris, France 195 Concordia University Wisconsin, Mequon, WI 53097, USA 196 Universit'a di Camerino, Dipartimento di Fisica, I-62032 Camerino, Italy 197 School of Physics Science and Engineering, Tongji University, Shanghai 200092, China 198 Southern University and A&M College, Baton Rouge, LA 70813, USA 199 Centre Scientifique de Monaco, 8 quai Antoine Ier, MC-98000, Monaco 200 Saha Institute of Nuclear Physics, Bidhannagar, West Bengal 700064, India 201 Institut des Hautes Etudes Scientifiques, F-91440 Bures-sur-Yvette, France 202 Indian Institute of Science Education and Research, Kolkata, Mohanpur, West Bengal 741252, India 203 Department of Astrophysics/IMAPP, Radboud University Nijmegen, P.O. Box 9010, 6500 GL Nijmegen, Netherlands 204 Consiglio Nazionale delle Ricerche - Istituto dei Sistemi Complessi, Piazzale Aldo Moro 5, I-00185 Roma, Italy 205 Hobart and William Smith Colleges, Geneva, NY 14456, USA 206 International Institute of Physics, Universidade Federal do Rio Grande do Norte, Natal RN 59078-970, Brazil 207 Museo Storico della Fisica e Centro Studi e Ricerche 'Enrico Fermi', I-00184 Roma, Italy 208 Lancaster University, Lancaster LA1 4YW, United Kingdom 209 Universit'a di Trento, Dipartimento di Matematica, I-38123 Povo, Trento, Italy 210 Indian Institute of Science Education and Research, Pune, Maharashtra 411008, India 211 Dipartimento di Fisica, Universit'a degli Studi di Torino, I-10125 Torino, Italy 212 Indian Institute of Technology, Palaj, Gandhinagar, Gujarat 382355, India 213 Departamento de Matem'atica da Universidade de Aveiro and Centre for Research and Development in Mathematics and Applications, Campus de Santiago, 3810-183 Aveiro, Portugal 214 Marquette University, 11420 W. Clybourn St., Milwaukee, WI 53233, USA 215 Universit'a di Firenze, Sesto Fiorentino I-50019, Italy 216 INAF, Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5, I-50125 Firenze, Italy 217 Indian Institute of Technology Hyderabad, Sangareddy, Khandi, Telangana 502285, India 218 INAF, Osservatorio di Astrofisica e Scienza dello Spazio, I-40129 Bologna, Italy 219 Andrews University, Berrien Springs, MI 49104, USA 220 Dipartimento di Scienze Aziendali - Management and Innovation Systems (DISA-MIS), Universit'a di Salerno, I-84084 Fisciano, Salerno, Italy 221 Van Swinderen Institute for Particle Physics and Gravity, University of Groningen, Nijenborgh 4, 9747 AG Groningen, Netherlands 222 Vrije Universiteit Brussel, Boulevard de la Plaine 2, 1050 Ixelles, Belgium 223 Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands  \n(Compiled: 11 May 2022)  \nThe second Gravitational-Wave Transient Catalog, GWTC-2, reported on 39 compact binary coalescences observed by the Advanced LIGO and Advanced Virgo detectors between 1 April 2019 15:00 UTC and 1 October 2019 15:00 UTC. Here, we present GWTC-2.1, which reports on a deeper list of candidate events observed over the same period. We analyze the final version of the strain data over this period with improved calibration and better subtraction of excess noise, which has been publicly released. We employ three matched-filter search pipelines for candidate identification, and estimate the probability of astrophysical origin for each candidate event. While GWTC-2 used a false alarm rate threshold of 2 per year, we include in GWTC-2.1, 1201 candidates that pass a false alarm rate threshold of 2 per day. We calculate the source properties of a subset of 44 high-significance candidates that have a probability of astrophysical origin greater than 0 . 5. Of these candidates, 36 have been reported in GWTC-2. We also calculate updated source properties for all binary block hole events previously reported in GWTC-1. If the 8 additional high-significance candidates presented here are astrophysical, the mass range of events that are unambiguously identified as binary black holes (both objects ≥ 3M glyph[circledot] ) is increased compared to GWTC-2, with total masses from  \n∼ 14M glyph[circledot] for GW190924 021846 to ∼ 182M glyph[circledot] for GW190426 190642. The primary components of two new candidate events (GW190403 051519 and GW190426 190642) fall in the mass gap predicted by pair-instability supernova theory. We also expand the population of binaries with significantly asymmetric mass ratios reported in GWTC-2 by an additional two events (the mass ratio is less than 0.65 and 0.44 at 90% probability for GW190403 051519 and GW190917 114630 respectively), and find that 2 of the 8 new events have effective inspiral spins χ eff > 0 (at 90% credibility), while no binary is consistent with χ eff < 0 at the same significance. We provide updated estimates for rates of binary black hole and binary neutron star coalescence in the local Universe.  \nPACS numbers: 04.80.Nn, 04.25.dg, 95.85.Sz, 97.80.-d 04.30.Db, 04.30.Tv", 'I. INTRODUCTION': 'We are in the era of gravitational wave (GW) astronomy, started by the Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) [1] and the Advanced Virgo [2] detectors. The first observing run (O1) of the advanced detectors yielded the first detection of GWs from a binary black hole (BBH), GW150914 [3]. By the end of O1, the LIGO Scientific and Virgo Collaboration (LVC) had reported on three BBH events [4]. The second observing run (O2) of the advanced detectors saw the first direct detection of GWs from a binary neutron star (BNS), GW170817 [5]. This event was also detected in electromagnetic waves [6], expanding the field of multimessenger astronomy to include GWs. By the end of O2, the LVC had reported on a total of ten BBHs and one BNS event, described in the first Gravitational-Wave Transient Catalog, GWTC1 [7]. The second Gravitational-Wave Transient Catalog, GWTC-2 [8], added 39 GW events from the first half of the third observing run (O3a), and included a total of 50 events. The GW data until the end of third observing run (O3) have been made available to the public by the LVC. Since the public release of the LIGO and Virgo data, groups other than the LVC have also performed analyses searching for GW signals [9-21] and reported additional candidate events in some cases.  \nGWevents between 1 April 2019 15:00 UTC and 1 October 2019 15:00 UTC (O3a) that passed a false alarm rate (FAR) threshold of 2 per year were presented in GWTC-2. Here, we present GWTC-2.1, a deep catalog that includes 1201 candidates passing a low-significance FAR threshold of 2 per day. Although most of the candidates in this catalog are noise events, they can be used for multimessenger searches by comparing against other astronomical surveys. Temporal and spatial coincidences between candidates in distinct astrophysical channels could lead to multimessenger discoveries [22, 23]. Multimessenger observations could enhance our understanding of the physical processes associated with such systems. Previous GW searches, both from the LVC [24] and independent groups [10, 13, 14, 24, 25], including the 3-OGC analysis of public data from O1 to O3a [17], have released subthreshold candidates. It is computationally  \nunfeasible to determine detailed source properties of the large set of subthreshold GW candidates, therefore we identify a subset of compact binary coalescence (CBC) candidates that have a probability of astrophysical origin p astro [26-28] greater than 0 . 5, and calculate the source properties of these events. This probability p astro uses both the signal rate in addition to the noise rate in order to determine the significance of events. There are 44 such candidate events, 36 of which have already been reported in GWTC-2 and their source properties have been described in detail [8]. We present the source properties with a consistent set of state-of-the-art waveform models for all of these candidates, discussing the properties of the 8 new events that have a p astro greater than 0 . 5 in detail in the body of the paper, and our results for the previously reported candidates in Appendix A. A subset of the 8 additional events have been found in the LVC search of O3a data [29] for faint gravitationally lensed counterpart images [30, 31], and in the independent 3-OGC [17] analysis. While the 8 new events presented here have a non-negligible probability of being from noise, some of these have astrophysically interesting source properties under the default prior. Two of the new candidates presented here have a primary component mass in the pair instability gap [32-40], and one of those shows support for high spin and unequal masses. We also find a new candidate whose masses are consistent with a neutron star-black hole binary (NSBH), although as in the case of GW190814 [41], we cannot rule out the possibility that the secondary component of the candidate could be a low-mass black hole.  \nIn this work, all the analyses make use of the final version of the strain data with improved calibration and noise subtraction, which includes non-linear subtraction around the 60 Hz frequency of the US power grid [42, 43]. The data used in this work have been released to the public [44-46]. We use three matched-filter pipelines for candidate identification: GstLAL [47-49], PyCBC [5054], and MBTA [55]. MBTA is reporting results from an archival search for the first time. Previously, in GWTC-2, only the GstLAL matched-filter pipeline included Virgo data; now all three pipelines analyze the data from all three detectors. For inferring the source properties, we use waveform models that include effects of spin-induced precession of the binary orbit, contributions from both the dominant and sub-dominant spherical harmonic modes, and tidal effects as appropriate [56-  \nThe paper is structured as follows: Sec. II describes the instruments and the data that are analyzed by the searches, including methods on calibration, data quality, and glitch mitigation. Sec. III describes the methods used by the search pipelines. Sec. IV describes the events in GWTC-2.1, comparison to GWTC-2, sensitivity of the search pipelines used, and inferred rates of BNSs and BBHs. Sec. V describes the methods used for estimating the source parameters of the GW candidates and results, and in Sec. VI, we discuss the astrophysically interesting events and their implications. In Sec. VII we describe the data products being released alongside this catalog and our conclusions. Finally, in Appendix A, we provide the source properties of events with p astro greater than 0.5 that have previously been described in GWTC-1 and GWTC-2. Companion results from the second half of the third observing run (O3b) are presented in the third Gravitational-Wave Transient Catalog, GWTC-3 [66].', 'II. INSTRUMENTS AND DATA': 'The Advanced LIGO [1] and Advanced Virgo [2] instruments are kilometer-scale laser interferometers. The two LIGO detectors are located in Hanford, Washington and Livingston, Louisiana in the United States, and the Virgo detector near Pisa in Italy. The advanced generation of interferometers began operations in 2015, and observing periods have alternated with commissioning periods since then [67]. In the time between O2 and the O3, all three detectors underwent significant upgrades that substantially increased their sensitivity [8, 68].  \nMajor instrumentation upgrades on the LIGO detectors included: replacement of main lasers to increase beam stability, replacement of test masses to lower scattering and absorption losses, installation of acoustic mode dampers to mitigate parametric instabilities [69], installation of a squeezed vacuum source to reduce quantum noise [70], addressing issues with scattered light [71], and implementation of improved feedback control systems for the instruments. Compared to the O2 run, the Hanford BNS range [50, 72] increased by 64% (from 66 Mpc to 108 Mpc), and for Livingston by 53% (from 88 Mpc to 135 Mpc).  \nFor Virgo, major upgrades included: replacement of the steel wire suspensions of the four test masses with fused-silica fibers [73], modification of the vacuum system to avoid dust contamination of the lowest suspension stage, replacement of the main laser to increase power, installation of a squeezed vacuum source to reduce quantum noise [74], improvements in beam stability [75], and addressing issues with scattered light. Compared to the O2 run, the Virgo BNS range increased by 73% (from 26 Mpc to 45 Mpc).  \nThe processing of the data recorded by the LIGO and Virgo detectors includes several steps that occur both in near-real time to allow for the broadcasting of pub-  \nlic alerts, and in higher latency to shape the final data set and update the catalogs of GW events. Raw data calibration and the subtraction of noise from known instrumental sources, documented in Sec. II A, occur first and the GW strain data, reconstructed independently in each detector, are then jointly processed. Significant GW candidates are vetted with several data quality tests as a part of the standard analysis procedure. This procedure is described in Sec. II B.', 'A. Calibration and noise subtraction': "The strain data used for astrophysical analyses is derived from the optical power variations at the output ports of the interferometers. Calibration of the raw photodetector signal to GW strain requires a detailed understanding and modeling of the control system and optomechanical response of the interferometers throughout an observing run. This allows for accurate and reliable calibration of the strain and also for quantifying its systematic and statistical uncertainty. The detailed procedure for the calibration and the determination of the systematic and statistical uncertainty of the LIGO and Virgo detectors for O3 can be found in [76-78].  \nThere are usually two calibrations applied to the data; a low-latency calibration and, if needed, an offline calibration. The low-latency (online) estimate of the strain uses the best models of the detector at the time of recording. However, over the course of any observing run, data drop-outs due to computer failures, incomplete modeling of the detector, and unknown residual systematic errors are often identified. The offline calibration incorporates the necessary corrections and improvements, producing a better calibrated strain with better known systematic uncertainty.  \nIn addition, numerous noise sources and calibration lines that limit detectors' sensitivity are measured and linearly subtracted from the data [42, 79-81]. This subtraction is performed online to generate the LIGO and Virgo low-latency strain data, and it is also performed when regenerating the LIGO offline strain data. Additionally, noise due to non-stationary coupling of the power mains with the LIGO detectors was subtracted from the offline data [42]. As an example of noise subtraction, Fig. 1 shows the improvement in the noise levels around the 60 Hz mains line in the Hanford detector, after non-linear noise subtraction was applied to the strain time series. Taking as a figure of merit the BNS range of the detectors [50, 72], the subtraction results in a median range increase of 0 . 9 Mpc for Hanford and 0 . 2 Mpc for Livingston.  \nIn GWTC-2, search pipelines and parameter estimation analyses used a mix of low-latency and offline calibrated frames. In contrast to this, all searches and analyses presented in this paper use strain data with the best available calibration and noise subtraction for each detector. For LIGO, this corresponds to the offline re-  \nFIG. 1. Comparison of the amplitude spectral density at Hanford around the 60 Hz mains line, between data with subtracted non-stationary noise and data with no subtraction. The data correspond to a typical one-hour observation-ready data stretch during O3a.  \n<!-- image -->  \ncalibrated data with 60 Hz non-linear subtraction. For Virgo, the online strain data stream was good enough to be used offline, except for the last two weeks of O3a which were reprocessed to improve subtraction of control and laser frequency noise [82]. The strain data used in this work are publicly accessible through the Gravitational Wave Open Science Center (GWOSC) [44].  \nIn addition, the LIGO offline data are accompanied with a much improved systematic and statistical error estimate compared to the online data. The probability distribution of the calibration uncertainty estimate for LIGO in O3a is characterized in [76], with the systematic error over the detectors' bandwidth being under 3% in magnitude and under 2 · in phase. The uncertainty in the Virgo strain data in O3a had a maximum systematic error over the detector's bandwidth under 5% in magnitude and under 2 · in phase [77]. Parameter estimation takes into account calibration uncertainties, as described in Sec. V. Given the size of calibration uncertainties in O3, there is no evidence that they have a significant impact on the inference of source parameters [83, 84].", 'B. Data quality, event validation & glitch mitigation': 'LIGO and Virgo data quality is continuously monitored during an observing run both on site and remotely, as reported in [85, 86]. This can include, for example, internal detector summary pages which detail the status of the detectors and interferometer subsystems [87, 88]. Feedback from GW searches also gives an indication of the impact of data quality on the sensitivity of a search. To exclude identified instances of poor data quality from  \nTABLE I. List of candidate-specific data usage and mitigation methods for parameter estimates. Only candidate events for which mitigation of instrumental artifacts was performed are listed. The glitch subtraction methods used for these candidate events are detailed in Sec. II B. The minimum frequency is the lower limit of data used in analyses of GW source properties for the listed interferometer.  \nthe searches and produce the results in Sec. III, we used the same methods and data products as reported for GWTC-2 [8]. The data quality products used in this work are publicly available [44, 45].  \nOnce a GW event has been identified by the search pipelines, we check the quality of data around the time of the event. We followed the same procedures outlined in [8] to validate the data quality around each new GW candidate reported in this paper. The aim of these validation procedures is to identify any instrumental or environmental noise that may impact the estimation of GW signal parameters. As summarized for GWTC2 [8], in some cases short-duration noise transients, or glitches [85, 89-91], can be subtracted from the data [9295]. When this is not possible, analyses use tailored configurations, for example, a modified low-frequency cutoff, to exclude data that could be corrupted by the presence of a nearby glitch. The full list of candidate events using candidate-specific glitch mitigation, along with the mitigation configuration, is found in Table I. These data, for the events where the glitch-mitigated data was used for the parameter estimation analysis in Sec. V, are publicly accessible [46]. No candidates in this catalog have clear evidence of instrumental origin identified through data quality validation studies.', 'III. CANDIDATE IDENTIFICATION': 'GW data is analyzed to search for candidates in two stages: first in low-latency in order to generate public alerts that subsequently trigger follow-up astronomical observations, and then in higher latency in the form of an offline analysis of the archival strain data, which is used to create GW catalogs. Five pipelines were used in real time to analyze O3 data: a minimally modeled generic transient search (coherent WaveBurst [96-100]), and four matched-filter [50, 51] pipelines (GstLAL [4749], MBTA [55], PyCBC [52-54, 101], and SPIIR [102]). Collectively, they identified 56 unretracted candidates during O3, 33 of which were found in O3a. GWTC-2 [8] presented 39 events identified by coherent WaveBurst, GstLAL, and PyCBC in the first offline search over O3a.  \nWe present here results from a refined offline search of O3a. The search employs three matched-filter pipelines: GstLAL, PyCBC, and MBTA [55], marking the first time that MBTA results from archival data are presented and included in a GW catalog. All three pipelines analyze the data from all three detectors. While GWTC-2 imposed a FAR ceiling of 2 per year on candidates, here we release a deep list of GW candidates with a FAR smaller than 2 per day [103]. In addition, we identify the 44 CBC candidates with an estimated p astro greater than 0 . 5 (Table II). There are also 2 candidates with p astro below 0 . 5 that do meet the FAR criterion used in GWTC-2; these are presented as marginal candidates. This GW catalog contains the largest number of candidates with p astro greater than 0 . 5 to date.  \nIn Sec. III A, we first lay out a general description of matched filter searches and in Sec. III B, we describe the methods employed by the three CBC searches used in this work. We describe the search results in the following Sec. IV.', 'A. Matched-filter searches': "The matched-filter method relies on having a model of the signal, as a function of the physical parameters. The parameters include those that are intrinsic to the source: two individual component masses m 1 , m 2 and two dimensionless spin vectors glyph[vector] χ 1 , glyph[vector]χ 2 (related to each component's spin angular momentum glyph[vector] S i by glyph[vector] χ i = c glyph[vector] S i / ( Gm 2 i )), and seven extrinsic parameters that provide the orientation and position of the source in relation to the Earth: the luminosity distance D L , two-dimensional sky position (right ascension α and declination δ ), inclination between total angular momentum and line-of-sight θ JN , time of merger t c , a reference phase φ , and polarization angle ψ . The search pipelines create a template bank [104-106] of GW waveforms covering the desired intrinsic parameter space, and use these to filter against the data and produce signal-to-noise ratio (SNR) time series. The component masses describing template waveforms are affected by source redshift z as m det i = (1 + z ) m i .  \nFor each set of intrinsic parameters, extrinsic parameters affecting the signal's amplitude and phase may be maximized over analytically [50], if the signal can be approximated as a pure quadrupole mode, i.e. ( glyph[lscript], | m | ) = (2 , 2). In particular, for this search, the templates use only the dominant quadrupole mode and assume quasicircular orbits with component spins aligned with the total orbital angular momentum. Peaks in the resulting SNR time series are stored as triggers. GW candidates are formed by imposing consistency in time and in template intrinsic parameters between triggers in different detectors; in addition, GstLAL also considers noncoincident triggers as candidates [47].  \nWhen considering a single template in a single detector with stationary, Gaussian noise, the matched filter SNR is an optimal statistic for ranking candidates. However, additional terms are needed to optimize sensitivity in searches of real data covering a wide signal parameter space. To account for the multi-detector network, the distribution of signals over relative times, phases and amplitudes between detectors is considered [48, 54]. Since detector noise is not stationary or Gaussian, signalconsistency tests such as chi-squared [51] are calculated and used to rank candidates.  \nThe distribution of noise triggers may vary strongly over the template masses and spins; we then model its variation empirically, as a function of combinations of parameters that are typically well-constrained by GW measurements. The binary's chirp mass [107],  \nM = ( m 1 m 2 ) 3 / 5 ( m 1 + m 2 ) 1 / 5 , (1)  \ndetermines to lowest order the phase evolution during the inspiral, and is typically better constrained than the component masses. At higher orders, the binary phase evolution is affected by the mass ratio q = m 2 /m 1 (where m 2 ≤ m 1 ) and by the effective inspiral spin χ eff , defined as [108]  \nχ eff = ( m 1 glyph[vector] χ 1 + m 2 glyph[vector] χ 2 ) · ˆ L N M , (2)  \nwhere M = m 1 + m 2 is the total mass and ˆ L N is the unit vector along the Newtonian orbital angular momentum. Finally, the ranking of events by the search pipelines may account for an assumed prior distribution of signals over masses and spins [109, 110].  \nThe significance of each candidate event is quantified by its FAR, the estimated rate of events due to noise with equal or higher ranking statistic value. The FAR is calculated by each search pipeline by constructing a set of background samples designed to have the same distribution over ranking statistic as search events in the absence of binary merger GW signals.  \nBy considering also the expected distribution of GW signal events recovered by a given search, we may derive an estimate of the relative probabilities of noise  \n(terrestrial) origin p terr , and signal (astrophysical) origin p astro [26-28]. For the bulk of released events, detailed estimates of source parameters are not calculated. Therefore, based only on the matched-filter search results we also estimate the probability for each event to belong to three possible astrophysical binary source classes, labeled BNS, NSBH and BBH. The classes are defined by binary component masses: BNS corresponds to { m 1 , m 2 } < 3 M glyph[circledot] , NSBH to m 1 > 3 M glyph[circledot] , m 2 < 3 M glyph[circledot] , and BBH to { m 1 , m 2 } > 3 M glyph[circledot] . For MBTA, a 2 . 5 M glyph[circledot] cut is used instead of 3 M glyph[circledot] , with a gap to 5 M glyph[circledot] for BBH. These definitions are chosen for simplicity: they do not imply that every binary component within a given mass range is necessarily a neutron star (NS) or a black hole (BH). Such inference would ultimately require measurement of the effects of NS matter on observed signals, which is beyond the capabilities of the search pipelines. The probabilities for an event to belong to each class ( p BNS , p NSBH , p BBH , and p terr ) are calculated from the template masses and spins recovered by the searches, under the assumption that events from each class occur as independent Poisson processes. The calculation also requires the choice of a prior on the event counts in each category [28]. GstLAL used a uniform prior for the BNS and NSBH categories, and a Poisson-Jeffreys prior for the BBH category; MBTA used a uniform prior for the BNS category, and a Poisson-Jeffreys prior for the NSBH and BBH categories; and PyCBC used a Poisson-Jeffreys prior for all three categories. Given the number of candidates, the prior choice does not significantly impact the BBH results. Implementation details differ between pipelines, as summarized below; the resulting probability estimates are listed in Tables II and III.  \nWhile the p astro values given here represent our best estimates of the origin of candidates using the information available from search pipelines, they are subject to statistical (random) and systematic errors, as well as in some cases clearly differing for a given candidate between different pipelines. One such uncertainty arises from methods used to rank events between pipelines, including tests for noise artifacts: such tests, such as chi-squared statistics, will in general add (different) random variations to the ranking of a given event, in addition to their differing power in distinguishing signals from artifacts. For single-detector candidates, there is an additional inherent uncertainty in estimating the rate of comparable noise events, which may only be bounded to (less than) 1 per observing time. An inherent source of potential systematic error also lies in the search ranking statistic used in the calculation of p astro : such statistics are optimized to detect a specific (usually broad) distribution of signals over binary intrinsic parameters. The resulting p astro estimates may be biased if this distribution deviates significantly from the (unknown) true signal distribution. The risk of such bias is largest for regions of parameter space containing few, or zero, confirmed detections. For all these reasons, our current p astro values may be revised in the future, particularly as and when current  \nuncertainties in the true signal rate and distributions are eventually reduced.  \nWe next review specific methods used by individual matched-filter pipelines.", 'B. Search pipelines': 'In this section we describe the pipelines that were used to identify the candidates presented in GWTC-2.1.', '1. GstLAL': 'The GstLAL analysis used in this search is largely similar to the one used in the previous analysis [8] and uses the same log-likelihood ratio L as the ranking statistic. Improvements have been made to the input data products generated by iDQ, the statistical inference framework to autonomously detect non-Gaussian noise artifacts in strain data based on auxiliary witness sensors [111, 112]. This iDQ timeseries is used to compute one of the terms in the log-likelihood ratio within the GstLAL analysis, that informs the search of the presence of non-Gaussian noise in close proximity to a GW candidate. Compared to GWTC-2, the timeseries generated by iDQ was reprocessed offline, having access to an expanded set of auxiliary witness sensors and trained with an acausal binning scheme [111]. As a result, the generated iDQ timeseries performs better in identifying noise artifacts in strain data. In addition, for GWTC2 the iDQ term was only used when ranking singledetector triggers, whereas now it is used for both coincident and single-detector triggers. Because of changes in the iDQ term, the empirically determined penalty for single-detector candidates had to be retuned compared to GWTC-2, and was increased to a penalty of ∆ L = -12 from ∆ L = -10. The single-detector event penalty is determined by comparing the recovery of simulated signals in single detector versus combinations of detectors and the sensitive volume-time for each configuration.  \nFor the GstLAL analysis, p terr and p astro shown in Tables II and III are estimated following the multicomponent population analysis [26, 113]. The response of each GstLAL template to each astrophysical source class, computed semi-analytically [110], is used in estimating these probabilities. The volume-time sensitivity of the pipeline used in this calculation is estimated based on simulated sources injected into the pipeline and is rescaled to the astrophysical distribution [114]. The volume-time ratios are used to combine triggers from various observation runs and perform a multicomponent analysis yielding p astro and merger rates [26, 113] inferred from O1 to O3a. The astrophysical distribution assumed in this analysis uses a log-uniform distribution for the source component masses, the component spins aligned with the orbital angular momentum, and a uniform distribution for the component spin magnitudes. The BH masses in BBHs  \nand NSBHs are distributed between 3 M glyph[circledot] and 300M glyph[circledot] with aligned component spins distributed in the range [ -0 . 99 , 0 . 99]. The NS masses in NSBHs and BNSs are distributed between 1 M glyph[circledot] and 3 M glyph[circledot] . In NSBHs, the NS spins are assumed to be aligned and distributed in the range [ -0 . 4 , 0 . 4], whereas, in BNSs the NSs are assumed to have small spins in the range [ -0 . 05 , 0 . 05]. These choices match previous analyses [8].', '2. MBTA': 'The Multi-Band Template Analysis (MBTA) pipeline [55] is based on matched filtering, relying on coincidences between triggers observed in different detectors. The version used for the offline search is close to the online version which contributed to the LVC public alerts [115]. The archival-search version benefits from offline-specific improvements, with a background estimate made over a longer duration, and with a reranking of the candidates using information collected not just before but also after the candidate.  \nThe parameter space covered by this analysis ranges from 1 M glyph[circledot] to 195 M glyph[circledot] for the primary (more massive) component, with total masses up to 200 M glyph[circledot] ; or from 1 M glyph[circledot] to 100 M glyph[circledot] for the primary when the mass of the secondary is between 1 M glyph[circledot] and 2 M glyph[circledot] . Component spins are aligned with the total angular momentum and are limited to 0.05 for objects below 2 M glyph[circledot] , and going up to 0.997 for objects above 2 M glyph[circledot] . The waveform used for the search is SpinTaylorT4 [116-118] if both binary masses are lighter than 2 M glyph[circledot] , and SEOBNRv4 [119] if the mass of one of the components is above 2 M glyph[circledot] . The total number of templates in the bank used is 727,992. The SNR threshold for recording triggers in each detector is 4.5, or 4.8 if one of the components is above 2 M glyph[circledot] .  \nThe FAR is calculated for each coincident event by forming random coincidences among single detector background triggers. This computation is performed independently for three large regions of the parameter space bounded by a 2 M glyph[circledot] limit for the mass of each component. These three regions are allowed to contribute equally to the background, while within each of them we sum the background contributions from all the templates.  \nThe p BNS , p NSBH , p BBH , and derived p astro quantities are computed as the fraction of recovered simulated events, representative of an astrophysical population, to this foreground plus background estimate provided by the pipeline [120]. The parameterizations of the populations are described in Sec. IV D, with the Power Law + Peak model used for BBH [121]. The rate of each type of source is adjusted using a multicomponent population analysis [26]. To follow the population and background evolution across the parameter space, 165 subregions are used. This finer resolution has the benefit of revealing events in population-rich areas, even if the overall background rate for their ranking statistic value is larger than few per year, as in the case of the high mass BBH event', '3. PyCBC': "In previous LVC searches [4, 7, 8, 122], the offline PyCBC [53, 123] pipeline has analyzed data only from the two LIGO detectors. In this analysis, PyCBC was extended to search data from the three-detector LIGOVirgo network, along with updates to the event ranking statistic [101] and the p astro calculation and a new method to estimate source class probability [124].  \nThe PyCBC search uses the same template bank as in GWTC-2 [8], constructed using a hybrid geometricrandom algorithm outlined in [125, 126]. Peaks in SNR time series exceeding a threshold of 4 constitute singledetector triggers. Two-detector coincident events are formed from triggers with the same component masses and spins with a physically allowed time difference between detectors, allowing for timing errors. Threedetector triple coincidences require triggers in all pairs of detectors to pass this consistency test.  \nThe detection statistic is given by the logarithm of the ratio of estimated signal event rate density to noise event rate density. We model the noise distribution in each detector as a decreasing exponential of the matched-filter SNR, reweighted based on a chi-squared signal-glitch discriminator [51, 127], with parameters that depend on the template intrinsic parameters. The signal distribution includes terms accounting for dependence on relative times of arrival, phases and amplitudes between detectors, as well as relative sensitivities of the participating detectors [54]. We estimate the FAR separately for each combination of detectors via time-shifted analyses [53, 128]. The significance for each candidate event is then found through addition of the FARs at the candidate's ranking statistic value over all active detector combinations [101].  \nIn addition to the generic PyCBC search, which covers the full parameter space [8] including a range of possible signal types, we also conduct a focused PyCBC BBH search [8, 14], capable of uncovering fainter BBH mergers by imposing a prior form for the signal distribution over the template bank [109]. This search is targeted at systems with mass ratios from 1 to 1 / 3, primary component masses from 5 M glyph[circledot] to 350 M glyph[circledot] , and aligned, equal component spins from χ = -0 . 998 to 0 . 998.  \nThe inference of p astro and p terr for each candidate event employs a Poisson mixture model of signal and noise events [26-28]. Here, the distribution of signal events is estimated via a set of simulated signals analyzed by the pipeline, and the rate and distribution of noise events are estimated from time-shifted analyses [53]. In GWTC-2 the calculation was only performed on potential BBH events with template chirp mass above 4 . 35 M glyph[circledot] (which corresponds to equal 5 M glyph[circledot] component masses). Here, we include potential BNS and NSBH events by performing independent calculations over ranges of template chirp mass below 2 . 18 M glyph[circledot] (corresponding to equal  \n2 . 5 M glyph[circledot] components), and between 2 . 18 M glyph[circledot] -4 . 35 M glyph[circledot] , respectively. Although the implied signal distribution over template chirp mass does not correspond to any specific astrophysical model, it is adequate for assignment of p astro given the current knowledge of BNS and NSBH merger populations. Systematic biases in p astro calculation may arise if the (unknown) true mass distribution is different from that assumed. The calculation is also extended relative to previous analyses to account for different possible coincident combinations of detectors [129]. The results given here are obtained from events occurring during O3a only, except for the BNS region where prior information of 1 highly significant detection was applied to represent GW170817 [5].  \nThe estimation method for binary source class probabilities [124] uses the binary chirp mass as input, and assumes a uniform density of candidate signals over the plane of component masses { m 1 , m 2 } . Here we take the classes to be defined by boundaries between different types of binary component at 3 M glyph[circledot] . To estimate source chirp mass, we correct the search template masses for cosmological redshift, using an estimate of the luminosity distance derived from the search SNRs and the corresponding templates' sensitivity. We then derive the relative probabilities of each source class and enforce that the sum of astrophysical source probabilities is equal to p astro .", 'IV. SEARCH RESULTS': 'We recover 1201 candidates that have FAR less than 2 per day in any of the search pipelines. These events and their estimated source probabilities are shown in Fig. 2. The candidates are shown in decreasing order of p astro . The total sum of p astro represents the Poisson rate of sources that pass the FAR threshold of 2 per day in each source class per O3a experiment, as estimated by the search pipelines. We find that this corresponds to between 24.95-44.50 signals in the BBH class, 0.66-3.80 signals in the NSBH class, and 0.22-0.81 signals in the BNS class in O3a. The range represents the difference in the search pipelines. We do not consider the PyCBCBBH analysis in the estimate of the number of signals in the BNS class provided here, as the analysis does not search over the BNS parameter space. Names are marked for the candidate events with p BNS or p NSBH greater than 20%. The dashed vertical line shows the least significant event with p astro greater than 0.5. An estimate of the rate of sources in the subthreshold candidate list per O3a experiment is obtained by the contribution to the sum from events with p astro less than 0.5. This corresponds to between 2.55-12.40 signals in the BBH class, 0.36-2.39 signals in the NSBH class, and 0.02-0.49 signals in the BNS class in the subthreshold candidates in O3a.  \nWe find 44 high probability CBC candidates that have p astro greater than 0 . 5. These events are listed in Table II.  \nThis list includes 8 new candidates that were not present in GWTC-2 [8]. These are marked in bold in Table II. Out of the 44 candidates, 4 were found with significant SNR only in one of the detectors by the GstLAL search, which is the only pipeline that looked for GW signals in single-detector data. These are listed with a dagger ( † ) next to the FAR in Table II. For the majority of events listed in Table II, p astro ≈ p BBH ; the exceptions are listed in Table III, which provides the list of candidates that have p BNS or p NSBH greater than 0.01.', 'A. New high probability candidates': "We recover all the events found in GWTC-2 as having p astro above 0.5, with the exception of three: GW190424 180648, GW190426 152155, and GW190909 114149. Since the rate of BBH events detectable by the LIGO-Virgo detectors is greater than the rate of detectable BNS or NSBH events, the p astro for events in the BBH class is higher than that of the events in the BNS or NSBH class at a fixed FAR. Therefore, in switching to a p astro threshold from a FAR threshold, one can expect to add BBH events while dropping some low-mass events.  \nAll the 8 new candidates with p astro greater than 0.5 are classified as BBHs, that is, p BBH is greater than p NSBH and p BNS . Only one new candidate, GW190725 174728, has a non-negligible probability in a source class other than BBH, with non-zero p NSBH (Table III). Out of the 8 candidates, only two (GW190725 174728 and GW190916 200658) are assigned p astro > 0 . 5 by more than one pipeline. Differences between pipelines are expected, due to the effects of random noise fluctuations on the different ranking statistics used, and due to different assumed signal distributions and other choices. In principle, a more accurate assessment of the candidates' origins could be obtained by considering information from all pipelines; however, this is not currently implemented as a quantitative measure. One of the events, GW190917 114630, is identified as a BBH by the GstLAL pipeline, with p BBH = 0.77 (Table II). However, when its source properties are inferred by follow-up pipelines, the mass parameters are found to be consistent with NSBH systems. Had it been classified as an NSBH to begin with by the search pipeline, the resulting p astro would not have made the threshold of 0.5. There is also non-stationary noise in the LIGO Livingston detector at the time of this event, but we have no evidence that the FAR of the event is misestimated. Out of the 8 new candidates, 5 candidates (GW190426 190642, GW190725 174728, GW190805 211137, GW190916 200658, and GW190925 232845) were identified in the LVC search for gravitationally lensed candidates in O3a data [29], while 4 candidates (GW190725 174728, GW190916 200658, GW190925 232845, and GW190926 050336) were also  \nTABLE II. Above-threshold GW candidate list. We find 44 events that have p astro in at least one of the searches as greater than 0.5. Bold-faced names indicate the events that were not previously reported in GWTC-2 [8]. The candidates marked with an asterisk were first published in 3-OGC [17]. The second column denotes the observing instruments. Candidate events in GWTC-2.1 which do not meet the p astro threshold but were at the same time as above-threshold events are given in italics. The PyCBC and PyCBC-BBH network SNRs do not include detectors with SNRs below 4; these events are marked with double dagger ( ‡ ) next to their network SNR. The 4 events marked with a dagger ( † ) next to their FARs were found only in one detector by the GstLAL search. All four were detected using the data from LIGO Livingston. For the single-detector candidate events, the FAR estimate involves extrapolation. All single-detector candidate events in this list according to the FAR assigned to them are rarer than the background data of about 6 months collected in this analysis. Therefore, a conservative bound on the FAR for candidates denoted by † is ∼ 2 yr -1 . GstLAL FARs have been capped at 1 × 10 -5 yr -1 to be consistent with the limiting FARs from other pipelines. Dashes indicate that a pipeline did not find the event with a FAR smaller than the subthreshold FAR threshold of 2 per day.  \nprobabilities  \nsource  \ne  \nulativ  \nCum  \n10  \n.  \n0  \n1  \n.  \n0  \n0  \n.  \n1  \nN  \n0  \n10  \nBNS  \nNSBH  \nBBH  \nGW190531 023648  \nGW190814  \nBNS  \n= 0  \n.  \n81  \n1  \n10  \nEvents  \n(a) GstLAL  \nFIG. 2. Cumulative sum of p BNS , p NSBH , p BBH as a function of the candidates that pass a FAR threshold of 2 per day. The events are shown in decreasing order of p astro . The sum of the source probabilities shown here represents the estimated Poisson rate of sources in each source class per O3a experiment by the different search pipelines. An estimate of the rate of sources in the subthreshold candidate list is obtained by the contribution to the sum from events with p astro less than 0.5. This estimate yields between 2.55-12.40 signals in the BBH class, 0.36-2.39 signals in the NSBH class, and 0.02-0.49 signals in the BNS class in the subthreshold candidates in O3a. The dashed vertical grey line shows where this threshold is for each pipeline. Names are marked for the candidate events with p BNS or p NSBH greater than 20%, since these are of particular interest for cross-correlation studies.  \n<!-- image -->  \nBNS  \nNSBH  \nBBH  \nN  \nNSBH  \nN  \nBNS  \nNSBH  \nBBH  \nGW190924 021846  \n1  \n10  \nEvents  \n(d) PyCBC-BBH  \nN  \nN  \nBBH  \n= 34  \n.  \n59  \nNSBH  \n= 1  \n.  \n54  \nGW190425  \n2  \n10  \nprobabilities  \nsource  \ne  \nulativ  \nCum  \nprobabilities  \nsource  \ne  \nulativ  \nCum  \n10  \n.  \n0  \n1  \n.  \n0  \n0  \n.  \n1  \n10  \n.  \n0  \n1  \n.  \n0  \n0  \n.  \n1  \n0  \n10  \n0  \n10  \n= 0  \n.  \n66  \n= 0  \n.  \n49  \n10  \n1  \nEvents  \n(b) MBTA  \nN  \nN  \nN  \nBBH  \n= 29  \n.  \n66  \n2  \n10  \nBBH  \n= 44  \n.  \n5  \nNSBH  \n= 1  \n.  \n4  \nGW190725 174728  \n2  \n10  \nTABLE III. Source probabilities ( p BBH , p BNS , p NSBH ) for the high significance GW candidates listed in Table II for which p BNS or p NSBH is greater than 1%. For other events in Table II, p astro ≈ p BBH , and therefore we do not list them here. Results are provided from all three matched-filter pipelines. Dashes indicate that a pipeline did not find the event with a FAR smaller than the subthreshold FAR threshold of 2 per day. The classification provided here assumes a boundary of 3 M glyph[circledot] between NSs and BHs in the case of GstLAL and PyCBC, and 2 . 5 M glyph[circledot] in the case of MBTA.  \nindependently identified and presented in 3-OGC [17]. The source properties of all 8 candidates are discussed in Sec. V D.", 'B. GWTC-2 candidates with p astro < 0 . 5': 'The three events in GWTC-2 that have a p astro smaller than 0 . 5 in GWTC-2.1 analyses are:  \nGW190424 180648 : This event was found by GstLAL as a single detector BBH event in Livingston. However, the data surrounding this event recorded periodic glitching from a camera shutter and iDQ (Sec. III B 1) heavily downranked the timespan surrounding this event [112]. Figure 4 in [112] shows both the inspiral track and the surrounding glitches in the timefrequency spectrogram surrounding this event and the response of iDQ. While the down-ranking due to iDQ for this particular event remains largely the same between GWTC-2 and GWTC-2.1, the retuning of the singles penalty (Sec. III B 1) in GstLAL for GWTC-2.1 caused the significance of the event to go down. Consequently, in GWTC-2.1, this event does not meet either the FAR threshold of 2 per year or the p astro threshold of 0 . 5.  \nGW190426 152155 : This event is in the marginalsignificance candidate list for GWTC-2.1 (Table IV); the FAR is similar to the one in GWTC-2 and still passed the threshold of 2 per year considered in the previous catalog. However, based on the masses recovered by the pipeline, it is assigned to the NSBH class with p NSBH = 0 . 14. The low p astro in the NSBH class is due to the fact that the inferred rate of detectable NSBHs is lower than that of detectable BBHs.  \nGW190909 114149 : This candidate BBH event was found as a coincident event in Hanford and Livingston detectors by GstLAL. It is recovered now with smaller SNR in the Hanford detector and is therefore ranked lower.', 'C. Marginal-significance candidates': 'The two GW candidates that satisfy the FAR criteria used by GWTC-2, but do not have p astro greater than 0.5 are listed as marginal candidates in Table IV. Both these events were detected by GstLAL with a small FAR, and were assigned to the NSBH class with p astro and p NSBH smaller than 0.5. Since the rate of detectable signals in the NSBH class is smaller than that in the BBH class, the p astro for these are smaller than they would be in the BBH class at the same FAR.', 'D. Search sensitivity': 'As in GWTC-2 [8], we quantify the sensitivity of the search via a campaign of simulated signals injected into the O3a data and analyzed by the search pipelines. We use a BBH signal distribution adjusted over that used for GWTC-2 to give more even coverage of the inferred distribution from [121], changing specifically the distributions over binary mass ratio and redshift. In addition to the BBH set, we also inject BNS and NSBH sets of simulated signals into the data. The sets are generated in two stages: first, points are sampled out to the maximum redshift considered for each set, then the samples are reduced to sets of potentially detectable signals by imposing that the expected LIGO Hanford-LIGO Livingston network SNR, calculated using a representative noise power spectral density (PSD), be above a threshold of 6. Although this threshold is below the matched-filter SNRs of events we consider as high-significance candidates, for detection thresholds corresponding to FARs significantly higher than 2 per year (the value used in GWTC-2), the cut may remove a non-negligible fraction of potentially detectable signals, due to random fluctuations in matched-filter SNR. The results of this simulation campaign for all the search pipelines have been made available [130].  \nTABLE IV. Marginal-significance GW event candidate list. There are 2 candidates that are found in at least one of the searches with a FAR less than 2 per year, but with a p astro smaller than 0.5 in all searches. The candidate in bold, GW190531 023648, is a new candidate identified in GWTC-2.1, not included in GWTC-2. The column max p astro shows the astrophysical class assigned with highest probability. Both candidates are detected by GstLAL with a small FAR, and are assigned to the NSBH class with p astro and p NSBH smaller than 0.5.  \nThe BNS signals are generated using the SpinTaylorT4 waveform model [116, 118], while the BBH and NSBH sets are generated using the SEOBNRv4PHM model [6062]. For simulated signals with redshifted total mass below 9 M glyph[circledot] , the SEOBNRv4P model without higher-order multipole emission was used, as higher-order multipoles would lie above the data sampling Nyquist frequency. The component spin magnitudes | χ | are distributed uniformly up to a maximum of 0 . 4 for NS components and 0 . 998 for BBH, with isotropically distributed orientations.  \nThe signal distributions over sky direction and binary orientation are isotropic. The distributions over redshift are proportional to the comoving volume element d V c / d z , multiplied by a factor (1 + z ) -1 accounting for time dilation, and by a factor (1+ z ) κ modeling possible evolution of the comoving merger rate density with redshift (as in Appendix E of [121]). A summary of the distributions of the three injection sets is given in Table V.  \nGiven the merger distribution used for each injection set, the sensitivity of each search over the O3 data is quantified by relating the expected number of detections, at a specified significance threshold, to the local astrophysical merger rate as N det = V R ( z = 0), where V is an effective sensitive hypervolume with units of volume × time. This effective hypervolume is estimated by counting the number of injected signals that are detected at the given threshold, here a FAR of 2 per year.  \nIn addition to assumed merger distributions that follow those used for the injection sets, we also provide V for a fiducial BBH population model representative of those found to have high posterior probability in our population analysis of GWTC-2 [121]. We choose the Power Law + Peak model (defined in Appendix B.2 of [121]) with parameters α = 2 . 5, β = 1 . 5, m min = 5M glyph[circledot] , m max = 80M glyph[circledot] , λ peak = 0 . 1, µ m = 34M glyph[circledot] , σ m = 5M glyph[circledot] , δ m = 3 . 5 M glyph[circledot] , setting the redshift evolution to κ = 0. The sensitivity for this BBH population is evaluated via importance sampling [114, 131] implemented via GWPopulation [132]. The effective hypervolume for each search and signal population is given in Table V.', 'E. Rates of BBH and BNS events': 'The rates of BBH and BNS binary mergers in the local Universe were estimated in a companion paper [121] to GWTC-2, using the count of detected events with FAR below 1 per year, combined with estimates of search sensitivity to the respective populations. The BBH rate estimate was marginalized over uncertainties in the parameters of the population models used, while the BNS rate estimate assumed a population uniform in component masses between 1 M glyph[circledot] and 2.5 M glyph[circledot] . The merger rate of NSBHs was recently calculated following the discovery of GW200105 162426 and GW200115 042309 [133], and we do not update it here.  \nHere, we present complementary BBH and BNS rate estimates based solely on the matched filter search pipeline outputs, with methods that allow us to incorporate a large number of likely noise (background) events [26] and thus avoid potential bias due to an arbitrary choice of significance threshold. Such methods allow for both foreground (signal) and background event distributions with a priori unknown rates, considered as independent Poisson processes. Furthermore, for the GstLAL pipeline we employ a multicomponent mixture analysis [113] to estimate the rates of events in several astrophysical classes (BNS, NSBH, and BBH) and terrestrial. Every trigger is assigned probabilities of membership in each class, as described in Sec. III B 1. For the MBTA and PyCBC rate estimates, only the BBH class is considered.  \nThe merger rate estimate then arises from the number of search events assigned to each class, divided by the estimated search sensitivity obtained via injection campaigns re-weighted to an astrophysical population model [114], as discussed in the previous section. The population models used here to quantify search sensitivity are in general different from those used to obtain source classification probabilities, described in Sec. III A.  \nIn both the BBH and BNS cases, as for other rate interval estimates derived from search results [7], a PoissonJeffreys ( ∝ R -1 / 2 ) prior was used. The choice of prior has little influence on estimated BBH rate due to the large count of signals, but it has a nontrivial effect on the BNS rate estimate as compared to, for instance, a uniform prior.  \nBBH merger rate estimates are provided by the Gst-  \n|  \n|  \n3  \nTABLE V. Measures of sensitivity for the search pipelines. We state the sensitive hypervolume V for each of four assumed signal populations: a BBH population following the injected distribution, a BBH population given by the Power Law + Peak model of [121], and BNS and NSBH populations following the injected distributions. We give estimates for each search pipeline independently at a FAR threshold of 2 per year, and for all pipelines combined, i.e. counting all injections detected in at least one pipeline at the given threshold.  \nLAL, PyCBC-BBH and MBTA pipelines. The astrophysical population assumed for measuring search sensitivities is given by the Power Law + Peak model of [121] with fiducial parameters as in Sec. IV D. The resulting merger rates are 25 . 0 +7 . 2 -6 . 1 Gpc -3 yr -1 for GstLAL, 26 . 0 +8 . 2 -6 . 8 Gpc -3 yr -1 for PyCBC-BBH and 25 . 6 +9 . 6 -7 . 8 Gpc -3 yr -1 for MBTA. These estimates are fully consistent with the estimate of 23 . 9 +14 . 3 -8 . 6 Gpc -3 yr -1 as derived in [121] using only significant (FAR < 1 yr -1 ) events, and allowing for uncertainties in the population model parameters. Following [121], we have not included the effect of calibration uncertainties in our rate estimates. A full quantitative analysis of such uncertainties would require accounting for possible frequency- and time-dependent amplitude systematic errors [76]; these are typically ∼ 3% or less, corresponding to a glyph[lessorsimilar] 10% sensitive volume uncertainty which remains subdominant to the Poisson uncertainty in the signal counts [121].  \nSince the only significant event consistent with BNS merger in O3a, GW190425, was observed in a single detector, it is present only in the GstLAL search results. Hence, we quote a BNS merger rate estimate only from the GstLAL pipeline, as we expect this to be more informative than estimates from pipelines that did not consider single-detector triggers. For measuring the search sensitivity to BNS mergers, we use the injected population described above in Sec. IV D, yielding an estimated merger rate 286 +510 -237 Gpc -3 yr -1 . This estimate is fully consistent within uncertainties with the simpler estimate of 320 +490 -240 Gpc -3 yr -1 derived using a fixed threshold in expected SNR to determine sensitivity to simulated signals [121].', 'V. ESTIMATION OF SOURCE PARAMETERS': "The physical parameters glyph[vector] ϑ describing each GW source binary, corresponding to individual entries from the list of events in Table II, are inferred directly from the data d and represented as a posterior probability distribution p ( glyph[vector] ϑ | d ). This probability distribution is evaluated through Bayes' theorem as  \np ( glyph[vector] ϑ | d ) ∝ p ( d | glyph[vector] ϑ ) π ( glyph[vector] ϑ ) , (3)  \nwith p ( d | glyph[vector] ϑ ) being the likelihood of d given a set of source parameters glyph[vector] ϑ , and π ( glyph[vector] ϑ ) being the prior probability distribution assumed for those parameters.  \nThe likelihood itself describes the assumptions of the underlying stochastic process generating the noise present in d from a given detector. This noise is assumed to be Gaussian, stationary and uncorrelated between pairs of detectors [134, 135], as further discussed in Sec. II B. This yields a Gaussian likelihood [136, 137], which for the i th detector used in a given analysis takes the form  \np ( d i | glyph[vector] ϑ ) ∝ exp [ -1 2 〈 d i -h i M ( glyph[vector] ϑ ) ∣ ∣ ∣ d i -h i M ( glyph[vector] ϑ ) 〉 ] , (4)  \nwith d i representing the data from this instrument. h i M ( glyph[vector] ϑ ) is the binary waveform model h ( glyph[vector] ϑ ) calculated for glyph[vector] ϑ after being projected onto the detector and adjusted to account for the uncertainty present in the offline calibration (as described in Sec. II) of d i [138]. The final likelihood is evaluated coherently across the network of available detectors and is obtained by multiplication of the likelihoods in each detector.  \nThe term from Eq. (4) in angle brackets, 〈 a | b 〉 , represents a noise-weighted inner product [136, 139]. In addition to d i and h i M ( glyph[vector] ϑ ), evaluating this inner product requires specification of the bandwidth to be used in  \nthe analysis as well as the PSD characterizing the noise process. The low-frequency cutoff used in our analysis is set at f low = 20 Hz. Time-domain waveform models are generated starting at a frequency f start such that the ( glyph[lscript], | m | ) = (3 , 3) spherical harmonic mode of the binary inspiral signal, as estimated from a set of preliminary analyses [7, 8], is present at f low . The highfrequency cutoff f high is selected for each analysis as f high = α roll -off f Nyquist such that the ringdown frequency of the ( glyph[lscript], | m | ) = (3 , 3) spherical harmonic mode, inferred from waveforms taken from the same set of preliminary analyses as mentioned above [7, 8], occurs below f high . The parameter α roll -off in this expression is a scale factor chosen in order to minimize the frequency roll-off effects caused by the application of a tapering window to the time-domain data [140]. The Nyquist frequency f Nyquist is then selected as the smallest power-of-two-valued frequency which together with α roll -off = 0 . 875 satisfies the constraint on f high specified above. Similarly, the duration of data d used in each analysis is determined from a requirement that the waveforms from previous analyses [7, 8] as evaluated from f low = 20 Hz and rounding up to the next power-of-two number of seconds, are contained in the selected data segment. The PSD for each event is inferred directly from the same data that is to be used in the likelihood, through the parametrized model implemented in BayesWave [141, 142]. From the inferred posterior distribution of PSDs, the median value at each frequency is then used in the final analysis [142, 143].  \nA GW signal emitted from a binary containing two BHs can be fully characterized by glyph[vector] ϑ containing a set of fifteen parameters, as introduced in Sec. III A, if the binary orbit is assumed to have negligible eccentricity. 1 The mass and spin of the post-merger remnant BH, together with the peak GW luminosity, are calculated from the initial binary parameters using fits to numerical relativity (NR) [144-149].  \nFor binaries expected to contain at least one NS, the time-evolution of the binary orbit is modified by the presence of matter and quantified in terms of the dimensionless quadrupole tidal deformability Λ 1 , 2 , adding one more parameter for each NS. In addition to the quadrupole tidal effects, other matter effects are parameterized in terms of Λ 1 , 2 using equation of state (EoS)-insensitive relations [150]. When a GW event is assumed to contain one or more neutron star, we do not report final masses or spins for the remnant object.", 'A. Waveform models': 'The binary properties of the observed GW events are characterized through matching against a set of waveform  \nmodels. For the events identified as BBHs, with both components inferred to have masses above 3 M glyph[circledot] , we use the independently developed IMRPhenomXPHM [56-59] and SEOBNRv4PHM [60-62] models. Both waveform models capture effects from spin-induced precession of the binary orbit, as well as contributions from both the dominant and sub-dominant multipole moments of the emitted gravitational radiation.  \nIMRPhenomXPHM [56] describes the GW signal from precessing non-eccentric BBHs and is part of the fourth generation of phenomenological frequency domain models. Precession is implemented via a twisting-up procedure, as for its predecessors IMRPhenomPv2 [151, 152] and IMRPhenomPv3HM [153, 154]. For this, an alignedspin model defined in the co-precessing frame is mapped through a suitable frame rotation to approximate the multipolar emission of a precessing system in the inertial frame. The stationary phase approximation is used to obtain closed form expressions in the frequency domain [155]. The description for the precession dynamics is derived using a multiple scale analysis of the post-Newtonian (PN) equations of motion [156]. The underlying aligned spin model for IMRPhenomXPHM is IMRPhenomXHM [57-59], which calibrates the ( glyph[lscript], | m | ) = (2 , 2) , (2 , 1) , (3 , 2) , (3 , 3) and (4,4) spherical harmonic modes to hybrid waveforms constructed from NR waveforms and information from the PN and effective-one-body (EOB) descriptions for the inspiral. IMRPhenomXHM represents the amplitudes and phases of spherical or spheroidal harmonic modes in terms of piecewise closed form expressions, with coefficients that vary across the compact binary parameter space, which results in extreme compression of the waveform information and computational efficiency.  \nSEOBNRv4PHM comes from another waveform family that is primarily based on the EOB formalism where the relativistic two-body problem is mapped to motion of a single body in an effective metric. In this framework, analytical information from several sources, such as PN theory and the test-particle limit, is combined in a resummed form. This is complemented with insights from NR simulations that accurately model the strong-field regime and incorporated into the EOB waveforms via a calibration procedure. We use the SEOBNRv4PHM [60-62] model, which includes precession and modes beyond the dominant quadrupole. This model is based on the aligned-spin model SEOBNRv4HM [63] and is calibrated to NR in that regime. It features full twospin treatment of the precession equations and relies on a twisting-up procedure to map aligned spin waveforms in the co-precessing frame to the precessing waveforms in the inertial frame [61, 62].  \nFor GW190917 114630, the less massive component is indicated to lie below 3 M glyph[circledot] and hence to have a strong likelihood of being a NS instead of a BH. Following the discussion for GW190814 [41], the nature of the less massive compact object in GW190917 114630 cannot be discerned from the GW data at present. This is primarily  \ndependent on the unequal masses [157-159] which will lead the merger of the binary to occur before an eventual NS component could have been tidally disrupted for any realistic NS EoS [157]. The lack of an observable NS disruption thus removes the potential for the observed signal to contain any additional information above a pointparticle baseline. For this reason, we present results for GW190917 114630 and GW190814 based on the BBH waveform models discussed above.  \nFor GW190425, the only O3a event in this catalog classified as a BNS, we follow the analysis presented in [8, 160] and report findings using the IMRPhenomP NRTidal waveform model [64, 65] which is based upon the BBH model IMRPhenomPv2 [151, 161, 162] with the addition of EoS dependent self-spin effects and contributions from tidal interactions tuned against NR and tidal EOB models. In order to reduce computational cost for the analysis of GW190425, a reducedorder-quadrature method was applied to the IMRPhenomP NRTidal model used [163, 164].', 'B. Sampling methods': 'To represent the continuous posterior probability density functions in glyph[vector] ϑ , we draw discrete samples from those distributions using three different methods. For analyses using IMRPhenomXPHM and IMRPhenomP NRTidal we use the Bilby inference package [140, 165], together with the nested sampling [166] method implemented in the Dynesty sampler [167], or the Markov-chain Monte Carlo sampler implemented in the LALInference package [137, 168-170]. For analyses using SEOBNRv4PHM, we use the RIFT package [171-173] which, due to a hybrid exploration of the parameter space split into intrinsic (masses and spins) and extrinsic parameters, is better suited for use with this more computationally expensive waveform model. The results from both analyses are collected and presented in a common format using the PESummary package [174, 175].', 'C. Priors': 'The prior probability on glyph[vector] ϑ is defined similar to GWTC2 [8] as uniform in spin magnitudes and redshifted component masses (specified in the geocenter rest frame), and isotropic in spin orientations, sky location and orientation of the binary orbit. We also assume uncorrelated and uniform prior probabilities for the tidal deformability parameters of the NSs in GW190425. The prior on the luminosity distance follows a distribution uniform in comoving volume, using a flat ΛCDM cosmology with Hubble constant H 0 = 67 . 90 kms -1 Mpc -1 and matter density Ω m = 0 . 3065 [176]. Masses reported in Sec. V D are defined in the rest frame of the original binary, and computed by dividing the redshifted masses by (1 + z ), with z calculated from the same cosmological  \nmodel. For GW190425 we perform two separate analyses, differing in the spin magnitudes they allow with a low-spin ( | glyph[vector] χ 1 | < 0 . 05) and a high-spin ( | glyph[vector] χ 1 | < 0 . 89), consistent with the choices made in GWTC-2 [8] for this binary.  \nAll analyses account for uncertainties in the reported strain calibration [76, 177]. The calibration uncertainties are described as frequency-dependent splines, defined separately for the strain amplitude and phase [178]. The coefficients at the spline nodes are allowed to vary alongside the binary signal parameters according to a Gaussian prior distribution set by the measured uncertainty at each node [138]. For analyses performed with the LALInference or Bilby inference packages, calibration uncertainties are marginalized over through direct sampling of the spline coefficients whereas RIFT analyses implement a likelihood reweighting method through importance sampling over an initial analysis where perfect calibration is assumed [179].', 'D. Source properties': 'In this subsection we report the inferred source properties of the 8 new events reported in Table II. The source properties for the BBH events from the first and second observation runs, reported in GWTC-1 [7], together with the remaining 36 events from Table II are reported in Appendix A. For the vast majority of the events reported both in this section and in Appendix A, the quoted source properties are taken from a set of posterior samples constructed from the two IMRPhenomXPHM and SEOBNRv4PHM analyses with each given equal weight. For a subset of events (GW151226, GW190413 052954, GW190413 134308, GW190421 213856, GW190426 190642, GW190521, GW190602 175927, GW190719 215514, GW190725 174728, GW190803 022701, GW190814, GW190828 063405, GW190828 065509, GW190917 114630, GW190926 050336 and GW190929 012149) the respective SEOBNRv4PHM analyses were incomplete at the time of journal submission, hence we report results from the IMRPhenomXPHM only for these events.  \nA selection of the one-dimensional marginal posterior distributions are shown in Fig. 3, with two-dimensional projections on the M -q and M -χ eff planes in Fig. 4 and Fig. 5 respectively. A more detailed set of results are presented in Table VI in the form of median and 90% credible intervals for the one-dimensional marginal posterior distributions for all 8 events. The complete multidimensional posterior distributions are available as part of the public data release accompanying this paper [180], as detailed further in Sec. VII.  \nTABLE VI. Median and 90% symmetric credible intervals for the one-dimensional marginal posterior distributions on selected source parameters for the 8 events that are new to this catalog with p astro > 0 . 5, highlighted in bold in Table II. The columns show source total mass M , chirp mass M and component masses m i , dimensionless effective inspiral spin χ eff , luminosity distance D L , redshift z , final mass M f , final spin χ f , sky localization ∆Ω and the network matched-filter SNR. The sky localization is the area of the 90% credible region. All quoted results are calculated from a set of posterior samples drawn with equal weight from the IMRPhenomXPHM and SEOBNRv4PHM analyses, with the exception of the SNRs that are taken from the IMRPhenomXPHM analysis alone (as RIFT, which was used for the SEOBNRv4PHM analysis, does not output that quantity). Additionally, following Sec. V D, the results presented for GW190426 190642, GW190725 174728, GW190917 114630 and GW190926 050336 are taken from an analysis using the IMRPhenomXPHM model only. A subset of the one-dimensional posterior distributions are visualized in Fig. 3. Two-dimensional projections of the 90% credible regions in the M -q and M -χ eff planes are shown in Fig. 4 and Fig. 5.  \nFIG. 3. Marginal posterior distributions on the primary mass m 1 , secondary mass m 2 , mass ratio q , effective inspiral spin χ eff and luminosity distance D L for the 8 events that are new to this catalog with p astro > 0 . 5, highlighted in bold in Table II. The vertical span for each region is constructed to be proportional to the one-dimensional marginal posterior at a given parameter value for the corresponding event. The posterior distributions are also represented numerically in terms of their one-dimensional median and 90% credible intervals in Table VI.  \n<!-- image -->  \n/circledot  \n/circledot', '1. Masses': 'The masses inferred for the 8 events presented in this section are generally comparable to, or higher, than the binaries reported in GWTC-2 [7, 8], as shown in Fig. 4. We find that the most massive BBH in GWTC-2.1 is GW190426 190642 with a total mass of 182 . 3 +40 . 2 -35 . 7 M glyph[circledot] and a remnant mass of 172 . 9 +37 . 7 -33 . 6 M glyph[circledot] ; it probably supersedes the previous most massive BBH  \nGW190521 2 with total mass of 153 . 1 +42 . 2 -16 . 2 M glyph[circledot] and a remnant mass of 147 . 4 +40 . 0 -16 . 0 M glyph[circledot] as reported in Appendix A 2. Both GW190426 190642 and GW190403 051519 join GW190519 153544, GW190521, GW190602 175927 and GW190706 222641 in a population of BBHs with over  \n50% posterior support for total mass M > 100M glyph[circledot] [8].  \nWhile the majority of the new events show a preference for mass ratios near unity, following the trend already observed in GWTC-2 [7, 8], both GW190403 051519 and GW190917 114630 recover posteriors with median q ∼ 1 / 5 with q = 0 . 23 +0 . 60 -0 . 12 and q = 0 . 21 +0 . 32 -0 . 09 respectively. As shown in Fig. 4, this constraint for unequal masses is robust at the 90% credible level for both GW190403 051519 and GW190917 114630. Although the contour indicating the 90% credible region for GW190403 051519 includes support at q ∼ 0 in Fig. 4, this is an artefact of the bounded kernel density estimation used to construct the contours, and for this event there are no samples at the prior boundary of q = 0 . 05.', '2. Spins': 'The best measured spin parameter for CBCs with observable inspiral signals tends to be the effective inspiral spin χ eff [181-183], introduced in Eq. (2), which is approximately conserved under spin-induced precession of the binary orbit [184-187]. Consequently, the angles between the spin-vectors and the orbital angular momentum at a formally infinite separation are well defined [187]. We therefore report χ eff , as well as the spin tilt angles themselves, at this fiducial reference point of infinite binary separation, or equivalently at an infinite time before the binary merger. The spins are evolved to infinite separation [188] using a precession-averaged evolution scheme [156, 187] where the orbital angular momentum is computed using higher-order PN expressions.  \nThe posterior distributions for χ eff for all 8 events are shown in Fig. 3 and Fig. 5. Again, the majority of the binaries are consistent with containing two non-spinning BHs with only GW190403 051519 and GW190805 211137 recovering a non-zero χ eff at 90% credibility. Both binaries report predominantly positive χ eff , further strengthening the pattern of a surplus of events with χ eff > 0 relative to those with χ eff < 0 reported in GWTC-2 [8] and investigated further in a companion paper [121].  \nSimilar to the compact objects reported in GWTC2 [7, 8], the majority of the compact-object spins reported in GWTC-2.1 have magnitudes consistent with zero. Two of the new events show evidence for large BH spins. In the case of GW190403 051519, 82% of the posterior probability lies in a region where at least one of the component spin magnitudes is above 0 . 8 whereas for GW190805 211137 this holds for 59% of the posterior probability.  \nFor binaries with very unequal masses, measurements of χ eff can translate into strong measurement constraints of χ 1 , the spin magnitude of the more massive object, whose spin angular momentum dominates over the secondary. This is the case for GW190403 051519, whose primary dimensionless spin is measured to be χ 1 = 0 . 89 +0 . 09 -0 . 31 . This represents the most nearly-extremal spin  \nobserved using GWs. Similarly, GW190805 211137 is recovered with χ 1 = 0 . 75 +0 . 22 -0 . 59 and GW190917 114630 with χ 1 = 0 . 23 +0 . 63 -0 . 21 . Both GW190403 051519 and GW190805 211137 are recovered as strongly preferring large χ 1 , with the inferred posterior distributions railing against the extremal BH-spin bound at χ 1 = 1. Hence, we also report the one-sided 90% lower bounds of χ 1 > 0 . 69 for GW190403 051519 and χ 1 > 0 . 29 for GW190805 211137. The posterior distributions for the spin magnitudes and tilt angles for these three events are shown in Fig. 6.', '3. Three-Dimensional Localization': 'As the 8 new events are all detected at relatively modest SNRs, together with several identifications as highmass BBHs, the inferred luminosity distances D L are generally larger than the binaries from GWTC-2 [7, 8]. GW190403 051519 is identified as probably the most distant event, with a recovered D L = 8 . 28 +6 . 72 -4 . 29 Gpc corresponding to a redshift z = 1 . 18 +0 . 73 -0 . 53 approximately twice as distant as the most distant events that were reported in GWTC-2 [7, 8] as also shown in Appendix A 2. In addition GW190426 190642, GW190805 211137, GW190916 200658 and GW190926 050336 all have inferred distances comparable to, or larger than, GW190413 134308, further highlighting the access to the distant Universe provided in GWTC-2.1.  \nAnother effect of the modest SNR of the new events is their comparatively poor localization on the sky. The best localized event is GW190805 211137 with a 90% credible region of ∆Ω = 1600 deg 2 . The credible intervals for the inferred distances and sky areas are shown in Table VI. The inferred localizations for all events are available as part of the accompanying data release to this paper, detailed further in Sec. VII.', '4. Waveform comparisons - Model systematics': 'The use of both the IMRPhenomXPHM [56-59] and SEOBNRv4PHM [60-62] models in the analyses of these events are motivated by the need to capture, and account for, potential differences in the inferred source parameters caused by the different methods used in the constructions of the models themselves. The vast majority of the posterior distributions reported in this section are constructed by combining an equal number of samples drawn from each of the IMRPhenomXPHM and SEOBNRv4PHM analyses [138]. For the majority of the 8 new events, the differences between the two single-model analyses, as well as to the combined-model results, are found to be comparable to the impact of model systematics effects identified in GWTC-2 [7, 8] being generally subdominant to the statistical uncertainty caused by the noisy data. For GW190403 051519 there are, however,  \nslight differences identified between the IMRPhenomXPHM and SEOBNRv4PHM analyses, most noticeably in the shape and structure of the marginal posterior distribution of some of the recovered mass and spin parameters. In these cases, the differences between analyses using either the IMRPhenomXPHM or SEOBNRv4PHM models are dominating over the other systematic uncertainties of the analysis, such as the estimation of the noise PSD. A deeper investigation into the broader impact of these model systematic effects, and their impact on the inferred source parameters for the population of GW events presented here, is left for a future study.', '5. Comparison to 3-OGC': 'Out of the 8 new events presented in this section, GW190725 174728, GW190916 200658, GW190925 232845 and GW190926 050336 were also independently identified and analyzed as part of 3OGC [17] using the PyCBC Inference package [189] and the IMRPhenomXPHM waveform model. We compare the inferred source properties for these events as presented in 3-OGC [190] and, to minimize potential model systematic effects, the IMRPhenomXPHM analysis performed for GWTC-2.1 presented here. Overall, we find a broad agreement between the two analyses. While there are differences found in the two sets of posterior distributions, they appear consistent within expectations from the differing choices of the analysis configurations and the assumed prior distributions between the two analyses for low SNR signals [191].', 'VI. ASTROPHYSICAL IMPLICATIONS': "Our analysis reports 8 new candidates with p astro > 0 . 5 in at least one pipeline. None of these candidates have p astro equal to 1 (Table II). Four of them were found only by a single analysis, and none were detected by all the pipelines (Table II). As discussed above in Sec. III A, p astro values are subject to statistical uncertainties, and are also subject to uncertainties arising from the true rate and distribution of signals. Such uncertainties are larger for events which, if astrophysical, fall within populations with few or zero significant detections. Here, we highlight such uncertainties for specific candidates, and discuss possible astrophysical implications under the hypothesis that the candidates do originate from compact object mergers.  \nParameter estimation indicates that two of the new candidates, GW190403 051519 and GW190426 190642, if astrophysical, have sources with a large total mass ( glyph[greaterorsimilar] 100 M glyph[circledot] , Table VI). Both were found only by the PyCBC-BBH analysis with a low SNR and relatively low p astro . They were also not recovered as significant events in the focused search of O3 data for intermediate-mass BH binaries [192]. Since there is only one significant de-  \ntection to date of a comparable BBH system, GW190521 [193, 194], the calculation of p astro for these candidates is subject to significant potential systematic error. These events are confidently above the break mass in the broken power law mass distribution model, at 39 . 7 +20 . 3 -9 . 1 M glyph[circledot] , or the Gaussian in the Power Law + Peak model at 33 . 1 +4 . 0 -5 . 6 M glyph[circledot] [121, 195, 196]. The estimated primary component masses, assuming astrophysical origin, are both above the lower edge of the pair-instability mass gap m low [197-200], even considering the large uncertainties about its value ( ≈ 40-70M glyph[circledot] , [32-40]). Adopting a rather conservative estimate of m low = 65M glyph[circledot] , the primary component of GW190403 051519 ( m 1 = 85 . 0 +27 . 8 -33 . 0 M glyph[circledot] ) has a probability 0.16 of being below m low with our standard mass prior, while the primary and secondary components of GW190426 190642 ( m 1 = 105 . 5 +45 . 3 -24 . 1 M glyph[circledot] and m 2 = 76 . 0 +26 . 2 -36 . 5 M glyph[circledot] ) are below m low with probabilities of 0.00078 and 0.30, respectively. The upper edge of the mass gap is even more uncertain, with theoretical predictions suggesting m up ≈ 120 M glyph[circledot] [201, 202]. The primary mass component of GW190403 051519 (GW190426 190642) has a probability 0.02 (0.25) of being above this value of m up . Thus, if astrophysical, GW190403 051519 and GW190426 190642 lie in the same group with GW190521: their primary components might be either inside or above the mass gap. Moreover, the estimated final mass of the merger remnant of GW190426 190642 ( M f = 172 . 9 +37 . 7 -33 . 6 M glyph[circledot] ) is in the intermediate-mass black hole regime (10 2 -10 5 M glyph[circledot] ).  \nThese features are suggestive of a dynamical formation channel, such as the hierarchical merger of smaller BHs [203-214] or repeated stellar collisions in dense star clusters [215-218]. In active galactic nuclei, the dense gaseous disk surrounding the central BH also triggers the hierarchical assembly of BHs [219-225]. Alternatively, extreme gas accretion from a dense gaseous disk [226-228] or from a stellar companion [229] might assist the growth of BH mass above the pair-instability threshold. Finally, primordial BHs might also have masses in the pair-instability gap [230, 231]. However, even the formation of BHs in this mass range from stellar collapse cannot be excluded, given the large uncertainties in stellar-evolution models [36, 39, 40, 232-234]. For example, very massive ( glyph[greaterorsimilar] 230 M glyph[circledot] ) extremely metal-poor ( Z < 10 -4 ) stars might turn into BHs with mass above the pair-instability gap [235-238].  \nParameter-estimation analysis indicates a large positive value of the effective inspiral spin χ eff = 0 . 68 +0 . 16 -0 . 43 and of the primary's spin magnitude χ 1 = 0 . 89 +0 . 09 -0 . 31 for GW190403 051519. From a theoretical perspective, BH spin magnitudes are highly uncertain [232, 239], with some models [240, 241] predicting very low spins ( ∼ 0 . 01) for single BHs because of efficient angular momentum transport in the stellar interior [242]. Observations of high-mass X-ray binaries in the local Universe indicate that BH spins can be nearly maximal [243, 244], while the majority of mergers in GWTC-2 are associated with low values of χ eff , with a slight preference  \nFIG. 4. Contours representing the 90% credible regions in the total mass M and mass ratio q plane for all events reported in this catalog. The events that are new to this catalog with p astro > 0 . 5, highlighted in bold in Table II, are highlighted in this figure following the same color scheme used in Fig. 3. The dashed lines act to separate regions where the primary and secondary binary component can have a mass below 3M glyph[circledot] .  \n<!-- image -->  \n/circledot  \nfor positive values [121]. Even if single stars form BHs with low spins [241], BHs in binaries may still develop high spins because of mass transfer [245], tidal interactions [239, 246, 247], or chemically homogeneous evolution [248, 249]. Alternatively, BHs born from the merger of two smaller BHs are expected to have high natal spins ( ∼ 0 . 7-0 . 9, [145, 146, 148]). This might suggest that the primary component of GW190403 051519 is a secondgeneration BH, which is also consistent with its large mass [205, 206, 214, 250, 251]. However, the positive effective inspiral spin χ eff of GW190403 051519 indicates a significant alignment of the spin vectors of (any of) the two components with the orbital angular momentum vector of the BBH. Nearly aligned spins are preferentially associated with isolated binary evolution [252, 253], while dynamically formed binaries tend to have an isotropically distributed spin orientations [254, 255].  \nFinally, GW190403 051519 is associated with a comparatively small mass ratio q (Fig. 3). Such low values of the mass ratio are unusual in isolated binary evolution, especially for the chemically homogeneous evolution [248, 256] but also for the common-envelope scenario [232, 257-260]. In contrast, low mass ratios are expected if the primary and secondary components are a secondand a first-generation BH, respectively [208, 209, 211], or if the primary BH is the result of a stellar merger in a young star cluster [216].  \nFour of the other new candidates (GW190805 211137, GW190916 200658, GW190925 232845,  \nGW190926 050336) fall in the mass range of the bulk of GWTC-2 BBHs, while the secondary component of GW190725 174728 has a 0.18 probability of lying in the lower mass gap ( ∼ 2-5 M glyph[circledot] ). The existence of a lower mass gap was inferred from observations of Galactic X-ray binaries [261-263], but there are a few observations of BHs with mass ≈ 3-4 M glyph[circledot] in non-interacting binary systems [264, 265] and microlensing surveys find no evidence for a mass gap between NSs and BHs [266, 267]. GWTC-2 BBH observations also suggest a dearth of systems between 2 . 6 M glyph[circledot] and 6 M glyph[circledot] [121, 268]. The only confirmed GW event in GWTC-2 with a component in the lower mass gap is GW190814 [41]. Numerical and theoretical models do not exclude the formation of compact objects in this mass range from a core-collapse supernova [269-272]. Other scenarios to explain the formation of binary compact objects in this mass range include mergers in multiple systems [273-276], primordial BHs [230, 277] and mass accretion onto a neutron star [278].  \nFinally, GW190917 114630 has component masses consistent with an NSBH ( m 1 = 9 . 7 +3 . 4 -3 . 9 M glyph[circledot] , m 2 = 2 . 1 +1 . 1 -0 . 4 M glyph[circledot] ), but was identified only as a BBH candidate, with p NSBH = 0 and p BBH = 0 . 77, by the pipeline that detected it (GstLAL). Since GW190426 152155 is a marginal candidate in this catalog, due to its low p astro (Table IV), GW190917 114630 is the only highprobability candidate with mass components in the NSBH range. However, as discussed in Sec. IV A, had  \nit been classified as an NSBH to begin with, its p astro measured by GstLAL would have been smaller due to the lower foreground rate of NSBHs as compared to BBHs in the detection pipelines, and not passed the threshold of 0.5 considered by the follow-up pipelines. As with the unusually high-mass BBH candidates, the assignment of p astro for NSBHs is subject to potential systematic error since no NSBH events have been confidently detected in the data set up to O3a used here, although see [66, 133] for NSBH discoveries in O3b. The masses and effective inspiral spin of this candidate are consistent with prior expectations for NSBH systems [257, 279-285]. Inferring the impact on the overall population of binary compact objects of the new candidates, including those with non-negligible probability of noise origin, requires a more involved analysis which is beyond this scope of this work [286, 287].", 'VII. CONCLUSION': "We have presented GWTC-2.1, which includes results from a refined search for CBCs in the first part of the third observing run of the Advanced LIGO and Advanced Virgo detectors. This is an extension to the previous GW catalog, GWTC-2 [8], over the same data, and provides a deeper list of GW candidates. The search we presented here was carried out using three matched-filter pipelines, MBTA, GstLAL, and PyCBC, and includes a list of candidates that have a FAR less than 2 per day in any of the pipelines. We provide detailed source properties of the 8 events that have p astro greater than 0.5 and were not present in GWTC-2. In addition, the source properties of previously reported events with p astro greater than 0.5 are presented in Appendix A.  \nOut of the 8 new candidates presented here, all events have masses consistent with BBH sources with the exception of GW190917 114630, whose source masses are consistent with being an NSBH (Sec. V D). If astrophysical, these events expand the scope of observed BBHs, with several binaries inferred at larger distances than previous detections and with both a new broader range of recovered BH masses and the addition of two binaries with significantly unequal masses. The primary components of two of the new candidates (GW190403 051519 and GW190426 190642) lie inside or, less likely, above the pair-instability mass gap. GW190403 051519 also shows support for high spin, unequal masses, and remnant mass in the intermediate-mass BH regime. These features are suggestive of dynamical formation, by hierarchical BH merger or by stellar collisions in dense stellar clusters or active galactic nuclei. However, we cannot exclude that GW190403 051519 and GW190426 190642 originated from isolated binary systems, because of the large uncertainties in the mass range of the pair-instability mass gap. Among the new candidates, GW190725 174728 shows some support for a secondary component mass in the lower mass gap (2-5M glyph[circledot] ). GW190917 114630, the  \nFIG. 6. The dimensionless spin parameters glyph[vector] χ i = c glyph[vector] S i / ( Gm 2 i ) estimated for individual binary components of selected sources. The radial distance of a given pixel on the left (right) of each disk, away from the center of the circle, corresponds to | glyph[vector] χ | for the more (less) massive compact object. Each pixel's angle from the vertical axis represents θ LS , the angle between the spin vector glyph[vector] S and the Newtonian orbital angular momentum. All pixels have equal prior probability with the shading denoting the relative posterior probability of the pixels, after marginalization over azimuthal angles. The events follow the same color scheme used in Fig. 3.  \n<!-- image -->  \nM  \n/circledot  \nFIG. 5. Contours representing the 90% credible regions in the plane of chirp mass M and effective inspiral spin χ eff for all events reported in this catalog. The events that are new to this catalog with p astro > 0 . 5, highlighted in bold in Table II, are highlighted in this figure following the same color scheme used in Fig. 3.  \n·  \n90  \n<!-- image -->  \n90  \n·  \nonly candidate with component masses consistent with an NSBH was initially classified as a BBH by the search pipeline, and therefore the p astro assigned to it is subject to systematics due to uncertainty in classification.  \nThe data products associated with GWTC-2.1 include candidate information from relevant search pipeline(s) and localizations for all events that pass a threshold of 2 per day in any search pipeline. The information from each search pipeline includes the template mass and spin parameters, the SNR time series, chi-squared values, the time and phase of coalescence in each detector, FAR, and p astro (Sec. III A). These data can be found at [103]. The source localizations are computed using the rapid localization tool BAYESTAR [288, 289], which was also used to produce the localizations in near real time during the observing runs while sending out GW alerts. We also release the results of the search pipelines running over simulated signal sets classified as BNS, NSBH, and BBH [130] that were used to calculate the sensitivities shown in Table V. For candidates that have a p astro > 0 . 5, we perform follow-up parameter estimation and also release the posterior samples associated with these events. These are available via [180]. Finally, the strain data for O3a used for the analyses in this paper are also available [44].  \nThe LIGO Scientific, Virgo and KAGRA Collaboration (LVK) have already announced the first observations from NSBHs [133] in the data from O3b, and the catalog that extends events up to O3b, GWTC-3 [66], has been released. GWTC-3 adds 35 GW candidates with p astro greater than 0.5 from O3b. O3 marks the most sensitive GW data published upon so far. The LIGO, Virgo, and KAGRA [290] detectors are currently offline and undergoing commissioning to enhance their sensitivities, and plan to all collect data simultaneously during the fourth observing run (O4) [67]. With further improvement in sensitivities and planning for pre-merger BNS detections [291-293], O4 offers improved prospects for GW and multimessenger astronomy, and promises to build upon our current knowledge of binary populations.", 'ACKNOWLEDGMENTS': "This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation. The authors also gratefully acknowledge the support of the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. The authors gratefully acknowledge the Italian Istituto Nazionale di Fisica Nucleare (INFN), the French Centre National de la Recherche Scientifique (CNRS) and the Netherlands Organization for Scientific Research, for the construction and operation of the Virgo detec-  \ntor and the creation and support of the EGO consortium. The authors also gratefully acknowledge research support from these agencies as well as by the Council of Scientific and Industrial Research of India, the Department of Science and Technology, India, the Science & Engineering Research Board (SERB), India, the Ministry of Human Resource Development, India, the Spanish Agencia Estatal de Investigaci'on, the Vicepresid'encia i Conselleria d'Innovaci'o, Recerca i Turisme and the Conselleria d'Educaci'o i Universitat del Govern de les Illes Balears, the Conselleria d'Innovaci'o, Universitats, Ci'encia i Societat Digital de la Generalitat Valenciana and the CERCA Programme Generalitat de Catalunya, Spain, the National Science Centre of Poland and the Foundation for Polish Science (FNP), the Swiss National Science Foundation (SNSF), the Russian Foundation for Basic Research, the Russian Science Foundation, the European Commission, the European Regional Development Funds (ERDF), the Royal Society, the Scottish Funding Council, the Scottish Universities Physics Alliance, the Hungarian Scientific Research Fund (OTKA), the French Lyon Institute of Origins (LIO), the Belgian Fonds de la Recherche Scientifique (FRS-FNRS), Actions de Recherche Concert'ees (ARC) and Fonds Wetenschappelijk Onderzoek - Vlaanderen (FWO), Belgium, the Paris ˆ Ile-de-France Region, the National Research, Development and Innovation Office Hungary (NKFIH), the National Research Foundation of Korea, the Natural Science and Engineering Research Council Canada, Canadian Foundation for Innovation (CFI), the Brazilian Ministry of Science, Technology, and Innovations, the International Center for Theoretical Physics South American Institute for Fundamental Research (ICTP-SAIFR), the Research Grants Council of Hong Kong, the National Natural Science Foundation of China (NSFC), the Leverhulme Trust, the Research Corporation, the Ministry of Science and Technology (MOST), Taiwan, the United States Department of Energy, and the Kavli Foundation. The authors gratefully acknowledge the support of the NSF, STFC, INFN and CNRS for provision of computational resources.  \nWe would like to thank all of the essential workers who put their health at risk during the COVID-19 pandemic, without whom we would not have been able to complete this work.  \nAnalyses in this catalog relied upon the LALSuite software library [170]. The detection of the signals and subsequent significance evaluations were performed with the GstLAL-based inspiral software pipeline [4749, 294], with the MBTA pipeline [55, 295], and with the PyCBC [53, 54, 101, 123] package. Estimates of the noise spectra and glitch models were obtained using BayesWave [92, 95, 296]. Source parameter estimation was performed with the Bilby library [140, 165] using the Dynesty nested sampling package [297], the RIFT library [171-173] and the LALInference library [137]. PESummary was used to post-process and collate parameter-estimation results [174]. The various  \nstages of the parameter-estimation analysis were managed with the Asimov library [298]. Plots were prepared with Matplotlib [299], seaborn [300] and GWpy [87]. NumPy [301] and SciPy [302] were used in the preparation of the manuscript.", '1. Binary black holes from the first and second observing runs': 'In order to provide a self-consistent set of source properties, inferred using the state-of-the-art BBH waveform models described in Sec. V A, we have reanalyzed the 10 BBH events observed during O1 and O2, and reported in GWTC-1 [7]. We present results combining samples from analyses using both the IMRPhenomXPHM and SEOBNRv4PHM, with the exception of GW151226 which, as mentioned earlier in Sec. V D, was analyzed using IMRPhenomXPHM only. As the BNS models available at the time of GWTC-1 still can be considered state-of-the-art in the NS-physics they describe, we have elected to not reanalyze the BNS event GW170817 as part of this study. For the source properties of GW170817, we instead refer to GWTC-1 [7] and its accompanying data release [303].  \nThe source properties for the 10 BBH events from the O1 and O2 are reported in Table VII, with a selection of the one-dimensional marginal posterior distributions shown in Fig. 7. The two-dimensional projections on the M -q and M -χ eff planes are shown as light-grey contours in Fig. 4 and Fig. 5 respectively. The full 15-dimensional posterior distributions are available as part of the public data release accompanying this paper [180], as detailed further in Sec. VII.  \nGenerally, the inferred source properties for these 10 BBHs are consistent with those presented in GWTC1 [7], but there are some new features worth highlighting. Where most binaries have a nominal support for χ eff = 0, GW151226 was in GWTC-1 identified to exclude this value at > 90% probability [7, 304], a conclusion which is strengthened further as of the analysis presented here in GWTC-2.1. The other BBH in GWTC-1 with only marginal support for χ eff = 0, GW170729, is now found to include support for negative χ eff in its 90% credible interval while also simultaneously preferring BH components with more unequal masses relative to what was inferred in GWTC-1.  \nIndependent analyses of these 10 events with the IMRPhenomXPHM model were previously presented in [305], showing broad consistency with the results presented in this section.', '2. Previously reported binaries from the first half of the third observing run': "The high-significance events from O3a are reported in Table II. Out of these events, 36 were included in GWTC-2 [8] with its accompanying data release [306]. Again, to ensure a self-consistent set of inferred source properties available for all CBC events observed by Advanced LIGO and Advanced Virgo, we provide a reanalysis of these 36 events using the BBH waveform models described in Sec. VA. We present results combining samples from analyses using both the IMRPhenomXPHM and SEOBNRv4PHM, with the exception of GW190413 052954, GW190413 134308, GW190421 213856, GW190521, GW190602 175927, GW190719 215514, GW190803 022701, GW190814, GW190828 063405, GW190828 065509 and GW190929 012149 which, as mentioned earlier in Sec. VD, were analysed using IMRPhenomXPHM only. As also described in Sec. V A, for the BNS event GW190425, the IMRPhenomP NRTidal waveform model [64, 65] was used. The analyses of these events also used the GW strain data described in Sec. II A, an additional improvement over the analyses presented in GWTC-2 [8]. For the events listed in Table I all analyses made use of data which included glitch subtraction or a reduction in the bandwidth available for astrophysical inference.  \nThe source properties for the 36 events from O3a are reported in Table VIII, with a selection of the onedimensional marginal posterior distributions shown in Fig. 8. The two-dimensional projections on the M -q and M -χ eff planes are shown as light-grey contours in Fig. 4 and Fig. 5 respectively. The full multi-dimensional posterior distributions are available as part of the public data release accompanying this paper [180], as detailed further in Sec. VII.  \nSimilar to the results presented in Sec. A 1, the vast majority of the inferred source properties for these 36 binaries are consistent with those presented in GWTC2 [8]. For a subset of binaries, their inferred masses have changed nominally with GW190706 222641 as of the GWTC-2.1 analysis preferring a higher total mass whereas both GW190521 and GW190929 012149 now are recovered as less massive than in GWTC-2. Additionally, GW190929 012149 as recovered in GWTC-2 had a comparatively broad and multimodal posterior distribution for its primary mass. The higher-mass mode is no longer present in the GWTC-2.1 analysis, which together with the secondary mass of GW190929 012149 remaining largely unchanged between the GWTC-2 and GWTC-2.1 analyses also leads to larger support for a more equalmass binary. GW190708 232457 on the other hand is now identified more predominantly with an unequal q distribution as compared to the broad support, with stronger preference for equal masses, reported in GWTC-2.  \nIndependent results with the IMRPhenomXPHM model for many of these events were previously pre-  \nTABLE VII. Median and 90% symmetric credible intervals for the one-dimensional marginal posterior distributions on selected source parameters for the 10 BBH events observed during the O1 and O2. These binaries were reported in GWTC-1 [7]. The columns show source total mass M , chirp mass M and component masses m i , dimensionless effective inspiral spin χ eff , luminosity distance D L , redshift z , final mass M f , final spin χ f , sky localization ∆Ω and the network matched-filter SNR. The sky localization is the area of the 90% credible region. All quoted results are calculated from a set of posterior samples drawn with equal weight from the IMRPhenomXPHM and SEOBNRv4PHM analyses, with the exception of the SNRs that are taken from the IMRPhenomXPHM analysis alone (as RIFT, which was used for the SEOBNRv4PHM analysis, does not output that quantity). Additionally, following Sec. V D, the results presented for GW151226 are taken from an analysis using the IMRPhenomXPHM model only. A subset of the one-dimensional posterior distributions are visualized in Fig. 7. Two-dimensional projections of the 90% credible regions in the M -q and M -χ eff planes are shown in grey in Fig. 4 and Fig. 5.  \nsented in 3-OGC [17]; other groups have also presented results with either the IMRPhenomXPHM, SEOBNRv4PHM or other precessing higher-mode models for, most prominently, the events GW190412 [307-310] and GW190521 [311-314]. While there is general agreement for the overall inferred source properties from many of these studies, there are significant differences present between them. These differences can however, as also explicitly stated in the studies themselves, be predominantly attributed to different prior assumptions or analysis configurations across the spread of the individual studies, in addition to the variance induced by waveform differences. This further highlights the need for the clear and public dissemination of both the exact analysis configurations used and the generated datasets containing the source properties inferred in order to encourage reproducibility and further model comparisons, especially as more events are added to the population of observed CBCs.  \nFIG. 7. Marginal posterior distributions on the primary mass m 1 , secondary mass m 2 , mass ratio q , effective inspiral spin χ eff and luminosity distance D L for the 10 BBH events observed during O1 and O2. The vertical span for each region is constructed to be proportional to the one-dimensional marginal posterior at a given parameter value for the corresponding event. The posterior distributions are also represented numerically in terms of their one-dimensional median and 90% credible intervals in Table VII.  \n<!-- image -->  \n/circledot  \n/circledot  \nTABLE VIII. Median and 90% symmetric credible intervals for the one-dimensional marginal posterior distributions on selected source parameters for the 36 events from Table II that were not reported in Table VI. The columns show source total mass M , chirp mass M and component masses m i , dimensionless effective inspiral spin χ eff , luminosity distance D L , redshift z , final mass M f , final spin χ f , sky localization ∆Ω and the network matched-filter SNR. The sky localization is the area of the 90% credible region. The results for the BBHs are calculated from a set of posterior samples drawn with equal weight from the IMRPhenomXPHM and SEOBNRv4PHM analyses, with the exception of the SNRs that are taken from the IMRPhenomXPHM analysis alone (as RIFT, which was used for the SEOBNRv4PHM analysis, does not output that quantity). Additionally, following Sec. V D, the results for GW190413 052954, GW190413 134308, GW190421 213856, GW190521, GW190602 175927, GW190719 215514, GW190803 022701, GW190814, GW190828 063405, GW190828 065509 and GW190929 012149 are from analyses using the IMRPhenomXPHM model only. For GW190425, we report results from the high-spin ( | glyph[vector] χ 1 | < 0 . 89) analysis, and since the calculation of the BH remnant properties is only valid for BBH model input those properties are excluded for this BNS signal. A subset of the one-dimensional posterior distributions are visualized in Fig. 8. Two-dimensional projections of the 90% credible regions in the M -q and -χ eff planes are shown in grey in Fig. 4 and Fig. 5.  \nFIG. 8. Marginal posterior distributions on the primary mass m 1 , secondary mass m 2 , mass ratio q , effective inspiral spin χ eff and luminosity distance D L for the 36 events from Table II that were not shown in Fig. 3. 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