Observing exoplanets through transmission spectroscopy supplies detailed information about their atmospheric composition physics and chemistry. Before the James Webb Space Telescope JWST these observations were limited to a narrow wavelength range across the nearultraviolet to nearinfrared alongside broadband photometry at longer wavelengths. To understand more complex properties of exoplanet atmospheres improved wavelength coverage and resolution are necessary to robustly quantify the influence of a broader range of absorbing molecular species. Here we present a combined analysis of JWST transmission spectroscopy across four different instrumental modes spanning 0.55.2 m using Early Release Science observations of the Saturnmass exoplanet WASP39 b. Our uniform analysis constrains the orbital and stellar parameters within subpercentage precision including matching the precision obtained by the most precise asteroseismology measurements of stellar density to date and it further confirms the presence of Na K HSUB2SUBO CO COSUB2SUB and SOSUB2SUB as atmospheric absorbers. Through this process we have improved the agreement between the transmission spectra of all modes except for the NIRSpec PRISM which is affected by partial saturation of the detector. This work provides strong evidence that uniform light curve analysis is an important aspect to ensuring reliability when comparing the highprecision transmission spectra provided by JWST.
['Astrophysics - Earth and Planetary Astrophysics']
A benchmark JWST nearinfrared spectrum for the exoplanet WASP39 b
{'No Header': '19', 'A Benchmark JWST Near-Infrared Spectrum for the Exoplanet WASP-39b': "A. L. Carter 1,2* & E. M. May 3* , N. Espinoza 1,4 , L. Welbanks 5,6 , E. Ahrer 7,8,9 , L. Alderson 10 , R. Brahm 11,12,13 ,  A.  D.  Feinstein 14,15 ,  D.  Grant 10 ,  M.  Line 5 ,  G.  Morello 16,17 ,  R.  O'Steen 1 ,  M. Radica 18,19 ,  Z.  Rustamkulov 20 ,  K.  B.  Stevenson 3 ,  J.  D.  Turner 21,6 ,  M.  K.  Alam 1,22, ,  D.  R. Anderson 8,7 ,  N. M. Batalha 2 ,  M. P. Battley 23 ,  D. Bayliss 8 ,  J.  L. Bean 14 ,  B. Benneke 24 ,  Z. K. Berta-Thompson 25 ,  J.  Brande 26 ,  E.  M.  Bryant 27 ,  M.  R.  Burleigh 28 ,  L.  Coulombe 24 ,  I.  J.M. Crossfield 26 ,  M.  Damiano 29 ,  J.-M.  Désert 30 ,  L.  Flagg 21 ,  S.  Gill 8,7 ,  J.  Inglis 31 ,  J.  Kirk 32,33 ,  H. Knutson 31 ,  L.  Kreidberg 9 ,  M.  López  Morales 34 ,  M.  Mansfield 35,6 ,  S.  E.  Moran 36 ,  C.  A. Murray 25 , M. C. Nixon 37 , D. J.M. Petit dit de la Roche 23 , B. V. Rackham 38,39,40 , E. Schlawin 35 , D.  K.  Sing 20,41 ,  H.  R.  Wakeford 10 ,  N.  L.  Wallack 22 ,  P.  J.  Wheatley 7,8 ,  S.  Zieba 9,42 ,  K. Aggarwal 43 , J. K. Barstow 44 , T. J. Bell 45 , J. Blecic 46,47 , C. Caceres 48,49,50 , N. Crouzet 42 , P. E. Cubillos 51,52 , T. Daylan 53,54 , M. de Val-Borro 55 , L. Decin 56 , J. J. Fortney 2 , N. P. Gibson 57 , K. Heng 58,8 , R. Hu 29,31 , E M.-R. Kempton 37 , P. Lagage 59 , J. D. Lothringer 1,60 , J. Lustig-Yaeger 3 , L.  Mancini 61,62,9 ,  N.  J.  Mayne 63 ,  L.  C.  Mayorga 3 ,  K.  Molaverdikhani 58,64 ,  E.  Nasedkin 9 ,  K. Ohno 65 , V. Parmentier 66 , D. Powell 34,6 , S. Redfield 67 , P. Roy 24 , J. Taylor 68,24 , X. Zhang 69  \n1 Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA 2 Department of Astronomy & Astrophysics, University of California, Santa Cruz, Santa Cruz, CA, USA 3 Johns Hopkins APL, Laurel, MD, USA  \n4 Department of Physics & Astronomy, Johns Hopkins University, Baltimore, MD 21218, USA 5 School of Earth and Space Exploration, Arizona State University, Tempe, AZ, USA 6 NHFP Sagan Fellow  \n7  \nCentre for Exoplanets and Habitability, University of Warwick, Coventry, UK  \n8 Department of Physics, University of Warwick, Coventry, UK  \n9 Max Planck Institute for Astronomy, Heidelberg, Germany  \n10 School of Physics, University of Bristol, Bristol, UK  \n11 Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Av. Diagonal las Torres 2640, Peñalolén,", 'Santiago, Chile': "12 Millennium Institute for Astrophysics, Chile  \n13 Data Observatory Foundation, Chile  \n14 Department of Astronomy & Astrophysics, University of Chicago, Chicago, IL, USA 15 Laboratory for Atmospheric and Space Physics, University of Colorado Boulder, Boulder, CO, USA 16 Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden 17 Instituto de Astrofísica de Canarias (IAC), Tenerife, Spain  \n18  \nDepartment of Physics, Université de Montréal, Montréal, Québec, Canada  \nTrottier Institute for Research on Exoplanets, Université de Montréal, Montréal, Québec, Canada  \n20  \nDepartment of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, USA  \n21  \nDepartment of Astronomy and Carl Sagan Institute, Cornell University, Ithaca, NY, USA  \n22  \nEarth and Planets Laboratory, Carnegie Institution for Science, Washington, DC, USA  \n23  \nDépartement d'Astronomie, Université de Genève, Sauverny, Switzerland  \n- 24 Department of Physics and Institute for Research on Exoplanets, Université de Montréal, Montreal, QC, Canada\n- 25 Department of Astrophysical and Planetary Sciences, University of Colorado, Boulder, CO, USA  \n26 Department of Physics & Astronomy, University of Kansas, Lawrence, KS, USA 27 Mullard Space Science Laboratory, University College London, Holmbury St Mary, Dorking, Surrey, UK 28 School of Physics and Astronomy, University of Leicester, Leicester  \n- 29 Astrophysics Section, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA  \n30  \nAnton Pannekoek Institute for Astronomy, University of Amsterdam, Amsterdam, The Netherlands  \nDivision of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA  \n32 Department of Physics, Imperial College London, London, UK  \n33 Imperial College Research Fellow  \n34 Center for Astrophysics | Harvard & Smithsonian, Cambridge, MA, USA  \n35 Steward Observatory, University of Arizona, Tucson, AZ, USA  \n36 Lunar and Planetary Laboratory, University of Arizona, Tucson, AZ, USA.  \n37 Department of Astronomy, University of Maryland, College Park, MD, USA  \n38 Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge,", 'MA, USA': '- 39 Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA, USA', '40 51 Pegasi b Fellow': '41 Department of Physics & Astronomy, Johns Hopkins University, Baltimore, MD, USA  \n42 Leiden Observatory, University of Leiden, Leiden, The Netherlands  \n43 Indian Institute of Technology, Indore, India  \n44 School of Physical Sciences, The Open University, Milton Keynes, UK  \n- 45 BAER Institute, NASA Ames Research Center, Moffet Field, CA, USA  \n46 Department of Physics, New York University Abu Dhabi, Abu Dhabi, UAE  \n47 Center for Astrophysics and Space Science (CASS), New York University Abu Dhabi, Abu Dhabi, UAE  \n48 Instituto de Astrofisica, Universidad Andres Bello, Santiago, Chile  \n49 Centro de Astrofisica y Tecnologias Afines (CATA), Casilla 36-D, Santiago, Chile  \n50 Nucleo Milenio de Formacion Planetaria (NPF), Chile  \n51 INAF - Osservatorio Astrofisico di Torino, Pino Torinese, Italy  \n52 Space Research Institute, Austrian Academy of Sciences, Graz, Austria  \nDepartment of Astrophysical Sciences, Princeton University, Princeton, NJ, USA', '54 LSSTC Catalyst Fellow': '55 Planetary Science Institute, Tucson, AZ, USA  \n56 Institute of Astronomy, Department of Physics and Astronomy, KU Leuven, Leuven, Belgium 57 School of Physics, Trinity College Dublin, Dublin, Ireland  \n58 Universitäts-Sternwarte, Ludwig-Maximilians-Universität München, München, Germany  \n59  \nUniversité Paris-Saclay, Université Paris Cité, CEA, CNRS, AIM, Gif-sur-Yvette, France  \n60 Department of Physics, Utah Valley University, Orem, UT, USA  \n61  \nDepartment of Physics, University of Rome "Tor Vergata", Rome, Italy  \n62 INAF - Turin Astrophysical Observatory, Pino Torinese, Italy  \n- Department of Physics and Astronomy, University of Exeter, Exeter, Devon, United Kingdom.\n- 63 64 Exzellenzcluster Origins, Garching, Germany\n- 65 Division of Science, National Astronomical Observatory of Japan, Mitaka-shi, Tokyo, Japan\n- 66 Université Côte d\'Azur, Observatoire de la Côte d\'Azur, CNRS, Laboratoire Lagrange, France\n- 67 Astronomy Department and Van Vleck Observatory, Wesleyan University, Middletown, CT, USA\n- 68 Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Oxford, UK\n- 69 Department of Earth and Planetary Sciences, University of California Santa Cruz, Santa Cruz, California, USA  \nObserving exoplanets through transmission spectroscopy supplies detailed information on  their  atmospheric  composition,  physics,  and  chemistry.  Prior  to JWST, these observations were limited to a narrow wavelength range across the near-ultraviolet to near-infrared, alongside broadband photometry at longer wavelengths. To understand more complex properties of exoplanet atmospheres, improved wavelength coverage and resolution  are  necessary  to  robustly  quantify  the  influence  of  a  broader  range  of absorbing molecular species. Here we present a combined analysis of JWST transmission spectroscopy across four different instrumental modes spanning 0.5-5.2 micron using  \n31  \n53  \nEarly  Release  Science  observations  of  the  Saturn-mass  exoplanet  WASP-39b.  Our uniform  analysis  constrains  the  orbital  and  stellar  parameters  within  sub-percent precision, including matching the precision obtained by the most precise asteroseismology  measurements  of  stellar  density  to-date,  and  further  confirms  the presence of Na, K, H 2 O, CO, CO 2 , and SO 2 atmospheric absorbers. Through this process, we also improve the agreement between the transmission spectra of all modes, except for the NIRSpec PRISM, which is affected by partial saturation of the detector. This work provides  strong  evidence  that  uniform  light  curve  analysis  is  an  important  aspect  to ensuring reliability when comparing the high-precision transmission spectra provided by JWST.  \nWASP-39b has a mass of ~0.28 𝑀 Jup , an equilibrium temperature of ~1100 K, and a highly inflated  radius  of  ~1.27 𝑅 Jup 14 ,  making  it  an  ideal  target  for  transmission  spectroscopy observations.  Past  optical  and  near-infrared  observations  of  WASP-39b  with  ground-based telescopes, the Hubble Space Telescope ( HST ),  and Spitzer have demonstrated evidence for strong  absorption  features  that  are  not  severely  affected  by  the  muting  effects  of  cloud extinction 15-18 ,  which  has  been  mirrored  in  the  initial  data  releases  for  each  of  our JWST observations across the near-infrared 5,7,10-12 . Furthermore, the late G-type host star WASP-39 (0.93 ± 0.03 𝑀 ⨀ ,  0.895 ± 0.23 𝑅 ⨀ )  is  known  to  be  relatively  inactive,  limiting  the  potential impact of stellar contamination 7,14,19,20 . The atmosphere of WASP-39b has a metal enrichment (metallicity) greater than that of its host star, although the range of metallicities which satisfy the observed JWST spectra differ between instrumental modes and can extend up to 100 × solar metallicity 5,7,11,12 .  Similarly,  the  measured  ratio  of  carbon-  and  oxygen-bearing  molecular species (C/O) in the atmosphere of WASP-39b seems to be sub-stellar or stellar, depending on the  instrumental  mode.  These  differing  compositional  measurements  are  likely  due  to  the preliminary modeling performed and the sensitivities of specific wavelength regions to relevant molecular tracers. Measurements of the metallicity and C/O ratio for an exoplanet are important indicators  of  its  bulk  atmospheric  chemistry 21-23 and  formation  history 24-26 ;  therefore,  these JWST observations must be analyzed in a homogeneous manner so that the complementary constraining power of their different resolutions and wavelength ranges can be fully realized, and the nature of WASP-39b can be best understood.  \nWe begin our data analysis with the extracted spectral time series as reported in the initial data release  publications  for  these  observations 5,7,11,12 .  At  this  initial  stage,  the  data  have  been corrected  for  both  background  and  1/ 𝑓 noise  (additional  correlated  read  noise  due  to,  for example, biases  in  the  detector  readout  electronics)  when  necessary.  As  there  are  multiple comparable reductions presented in each of these publications, we select the nominal case from each. Specifically, we choose the supreme-SPOON 5,27 reduction for Near-Infrared Imager and  Slitless  Spectrograph  (NIRISS)  Single  Object  Slitless  Spectroscopy  (SOSS) 3,4 ,  the Eureka! 28 reduction  for  Near-Infrared  Camera  (NIRCam)  F210M+F322W2 6,7 , the ExoTIC-JEDI 29 [V2] reduction for Near-Infrared Spectrograph (NIRSpec) G395H 8,9,12,13 , and the FIREFLy 30 reduction for NIRSpec PRISM 8-11 . As WASP-39b is the only exoplanet to date to have been observed with such a diversity in instrumental capability, this is the first opportunity for a one-to-one cross-comparison between these modes and a verification of their relative  performance. We extract seven separate white light curves from these data for our  \nanalysis  using Eureka! 28 :  two  from  the  separate  NIRISS  SOSS  Order  1  and  Order  2 spectroscopy,  one  from  the  NIRCam  F210M  photometry,  one  from  the  NIRCam  F322W2 spectroscopy, two from the NIRSpec G395H spectroscopy captured separately on the NRS1 and NRS2 detectors, and one from the NIRSpec PRISM spectroscopy (see Methods, Extended Figs 1-3, Extended Table 1).  \nTo  constrain  the  parameters  for  WASP-39b  and  its  host  star  we  perform  a  joint  fit  using juliet 31 to  the  seven JWST white  light  curves,  in  addition  to:  the  \' Transiting Exoplanet Survey  Satellite\'  (TESS) 32 light  curve,  six  separate  \' Next  Generation  Transit  Survey\' ( NGTS) 33 light  curves 7 ,  and  the  CORALIE  and  \'Spectrograph  for  the  Observation  of  the PHenomena of stellar Interiors and Exoplanets\' (SOPHIE) radial velocity measurements 14 . All light curve fits are displayed in Figure 1 and best-fit parameters are provided in the Table 1. With the combined constraining power of these data, we are able to obtain exquisite constraints on the WASP-39 system. The period of WASP-39b is constrained at sub-second precision (~0.3 seconds), with other physical and orbital parameters constrained at sub-percent precision (~0.1-0.5%). Of particular interest is the constraint on stellar density provided by the fitting, as it is constrained to ~0.3%-an equivalent precision to the most precise asteroseismology measurements made to date 34 . This is a direct consequence of the sampling of the transit events by  the  different JWST observations,  which  constrain  the  period  and  transit  duration  at unprecedented precision, and together with Kepler\'s third law, define the stellar density 35 . If such precisions are common for JWST white-light curves in general, then they could give rise to better constraints on orbital decay or transit timing variations, and improved stellar density measurements may improve constraints on system ages.  \nSpectrophotometric  light  curves  are  extracted  at  the  native  spectral  resolution  from  their corresponding spectral time series using the Eureka! 28 package, with orbital parameters fixed to the best-fit values from the white light curve fitting. This results in 1028 light curves for NIRISS  SOSS  ( 𝑅~ 350-1390, 𝜎 mean =310  ppm),  550  for  NIRCam  F322W2  ( 𝑅~ 850-1360, 𝜎 mean =294 ppm), 1163 for NIRSpec G395H ( 𝑅~ 1340-2630, 𝜎 mean =496 ppm), 147 for NIRSpec PRISM  ( 𝑅~ 20-290, 𝜎 mean =108 ppm), and an overall total of 2888 individual spectrophotometric light curves. As part of our analysis, we also investigate reductions at lower resolution binning schemes and find that unless the underlying limb-darkening parameters are fixed during light curve fitting, significant wavelength-dependent variations in excess of 150 ppm are present between native spectral resolution and 𝑅 =100 spectra (see Methods, Extended Figure  4).  The  combined  transmission  spectra  of  the JWST observations  from  this  work, alongside those of the initial data release publications, is displayed in Figure 2. The initial releases have different uncertainties at some locations as they are at a different resolution than the synthesized release.  \nThe measured transmission spectra from the initial data releases display clear offsets relative to each other, whereas such offsets are reduced for the synthesized spectra after following the joint light curve fitting procedure described above (Figure 2, also see Methods). In the most extreme case, the NIRSpec G395H and NIRISS SOSS spectra have a mean offset in their overlapping region of 343 ± 16 ppm when using the initial spectra, and 138 ± 16 ppm when using the synthesized spectra. This indicates that different assumptions and inferences during the  \nlight curve fitting process can significantly affect the final measured transmission spectrum, even with the constraining power of a single, highly precise, JWST white light curve. Despite these synthesizing efforts, offsets are still present between the different modes. Median offsets of the spectra from the higher resolution modes relative to the NIRSpec PRISM spectrum are shown in Figure 2c. Differences in the median offsets between the higher resolution modes and NIRSpec  PRISM  are  driven  by  the  wavelength-dependent  nature  of  NIRSpec  PRISM systematics, compounded with wavelength-independent offsets between the higher resolution modes (see Methods, Extended Figure 5, Extended Table 2).  \nWavelength-independent  offsets  are  commonly  seen  between  different  telescopes  and/or instruments and are typically due to different orbital or stellar parameters assumed, or different instrument sensitivities. However, an offset is present between NIRSpec PRISM and NIRSpec G395H, even though we use consistent orbital and stellar parameters, and the NIRSpec PRISM detector is the same as the short wavelength (NRS1) NIRSpec G395H detector. One potential explanation is that the low number of groups used in the NIRSpec PRISM observations may increase the influence of first-group effects and drive a more significant offset compared to the other observations, which utilize more groups. Such a shift has been observed in NIRCam transit observations 36 and may likely be present in this data also (see Methods). Upon applying a shift to the NIRSpec PRISM data it is possible to better match the other datasets across a broader wavelength range, however, a wavelength-dependent offset is still apparent from ~0.62.0  µm.  It  is  across  this  wavelength  range  that  the  NIRSpec  PRISM  data  are  affected  by detector saturation.  \nDespite an initial assessment that saturated data could be recovered for NIRSpec PRISM 11 , there is a clear discrepancy when compared to the unsaturated NIRISS SOSS data. In reality, the  complex  interaction  of  detector  saturation,  non-linearity,  first-group  effects,  and  pixel cross-talk modulate the measured transit depth as a function of wavelength. An investigation into determining a potential further correction to the saturated data is presented in the Methods section, Extended Figures 6-8; however, for the purposes of our model analysis we rely solely on the NIRISS SOSS measurements at the wavelengths impacted by saturation. For future analyses, we recommend taking significant caution when inferring atmospheric properties from data that have been directly influenced by saturation. Such a conclusion is in agreement with past  observations  using  earlier  generations  of  infrared  detectors,  for  which  modeling  the detector  response  to  saturation  was  difficult 37 , and  data  experiencing  saturation  were discarded 38 .  Even if absorption features are present across a region of partial saturation that qualitatively  match  the  predictions  of  an  atmospheric  model,  the  precise  structure  and amplitudes of those features may not be reliable. Where reliable inferences on the structure of an  absorption  feature  are  required,  instead  of  obtaining  saturated  NIRSpec  PRISM  data, observers could consider using multiple higher-resolution modes. For example, NIRISS SOSS and NIRSpec G395M/H cover a similar wavelength range as NIRSpec PRISM but saturate more slowly due to their higher resolving powers. Alternatively, the NIRSpec G140M/H mode could be utilized alongside NIRSpec PRISM, as its ~0.97-1.89 µm wavelength range spans the region of NIRSpec PRISM that has the highest throughput and is most prone to saturation, but offers a ~2 magnitude improvement in brightness limit.  \nThe  combined  transmission  spectra  exhibit  a  variety  of  spectroscopic  features  that  can  be attributed to absorption from elemental and molecular species. We investigate the origin of these spectroscopic features by comparing the observations to a self-consistent 1-dimensional radiative-convective photo-chemical-equilibrium (1D-RCPE) model that assumes a 10 × solar metallicity  and  a  sub-solar  carbon-to-oxygen  ratio  of  0.35  (Figure  3);  consistent  with  the inferred atmospheric properties from the initial data releases. We consider the possibility of inhomogeneous aerosols shaping the absorption features in our spectrum by post-processing the  1D-RCPE model with clouds and hazes resulting from a fit to the data (see Methods). Additionally, we allow for a uniform offset to the NIRSpec PRISM data relative to the chosen model and find a median value of -177 ppm. The resulting atmospheric model confirms that the spectral features are best explained by absorption due to Na, K, H 2 O, SO 2 , and CO 2 . We also note the presence of a narrow absorption excess at ~1.083 μm that is evident across all explored binning schemes and may be indicative of absorption from metastable Helium 39,40 , but  a  further  investigation  is  outside  the  scope  of  this  paper.  The  spectral  fit  additionally confirms the need for cloud extinction as expected from the relatively muted spectral features in  the  data.  A  more  detailed  analysis  of  these  data,  which:  covers  a  broader  variety  of atmospheric modeling methodologies, explores offsets for all of the JWST modes, and provides constraints  on  accessible  atmospheric  properties,  is  presented  in  a  companion  publication (Welbanks et al. Submitted).  \nImportantly,  we  find  that  a  uniform  analysis,  namely  joint  white  light  curve  fitting  for consistent orbital parameters, results in an improved agreement over previously independent analyses  between  all JWST observing  modes  considered,  with  the  exception  of  NIRSpec PRISM. Caution should be exercised when combining JWST spectra from different instruments without a uniform light curve analysis, particularly if those datasets have been analyzed by independent teams. Until a more comprehensive limb-darkening investigation is performed, fixing limb-darkening parameters to models rather than fitting for these parameters at high resolution is necessary to improve consistency between spectra of the same dataset at different resolutions.  Finally,  while  the  included  NIRSpec  PRISM  observations  were  impacted  by several  detector  effects,  particularly  due  to  saturation  (see  Methods),  this  mode  remains  a powerful tool for efficient characterization of planets around dimmer stars.  \nThe continued effort to understand how to best combine data from multiple instruments is important  to  accurately  characterize  exoplanet  atmospheres.  By  combining  their  differing capabilities  these  broad-wavelength,  high-precision,  and  high-resolution  measurements  will facilitate a wide range of model analyses, beginning with those presented in our companion publication (Welbanks et al. Submitted), and will greatly improve our understanding of the origins,  histories,  and  atmospheres  of  exoplanets.  Finally,  with  future  ultraviolet  and  midinfrared transmission measurements of WASP-39b also on the horizon ( HST GO-17162, JWST DDT-2783), we are poised to begin exploring the full potential of this new era of exoplanet characterization and the scientific advances that it can offer.', 'Data Reduction': "The data presented in this work were obtained from a selection of observations of WASP-39b from  the  Panchromatic  Transmission  sub-program  within  The JWST Transiting  Exoplanet Community Director's Discretionary ERS program 1,2 (ERS 1366; PIs: N. M. Batalha, J. L. Bean, K. B. Stevenson). This includes primary transit observations with: Near-Infrared Imager and Slitless Spectrograph (NIRISS) Single Object Slitless Spectroscopy (SOSS) 3-5 from July 26-27, 2022 (20:53 - 05:35 UT), Near-Infrared Camera (NIRCam) F210M+F322W2 6,7 from 22-23 July 2022 (19:28 - 03:40 UT), Near-Infrared Spectrograph (NIRSpec) G395H 8,9,12,13 from 30-31 July 2022 (21:45 - 06:21 UT), and NIRSpec PRISM 8-11 on 10 July, 2022 (15:05 23:39 UT). These observational modes span all three of JWST 's near-infrared instruments with resolving powers of 𝑅 ≃ 100-2700 depending on the mode, and have overlapping wavelength ranges within a combined range of 0.518-5.348 µm.  \nThe data reduction for this work begins with the extracted spectral time series as presented in the initial ERS publications for these observations 5,7,11,12 . At this stage, the data have undergone data processing steps such as detector-level corrections, ramp fitting, flat fielding, subtraction of background and 1/ 𝑓 noise, wavelength calibration, and spectral extraction. For a detailed account of the precise analysis steps taken for each instrumental mode, we refer the reader to the initial ERS publications.  \nAs the initial ERS publications provide a variety of different reductions to the data, spanning different pipelines and different methodologies, we select just a single reduction from each for our analyses. For NIRCam F322W2, NIRSpec PRISM, and NIRSpec G395H, we select the reduction which matches that chosen in the initial publication, but for NIRISS SOSS we adopt a different reduction due to improvements in the out-of-transit baseline scatter. Specifically, this corresponds to the Eureka! 28 reduction for NIRCam F322W2, the supreme-SPOON 5,27 reduction for NIRISS SOSS, the ExoTIC-JEDI [V2] 29 reduction for NIRSpec G395H, and the FIREFLy 30 reduction for NIRSpec PRISM. We note that while a single reduction pipeline may  be  desirable,  fundamentally  different  reduction  procedures  are  required  between instrumental modes, and a 'jack of all trades' pipeline will not necessarily produce the most consistent  results.  Further,  analyses  from  the  initial  data  releases 5,7,11,12 demonstrated  that different pipelines can reach a good agreement on the resulting spectra. Median out-of-transit stellar spectra for each of the selected reductions are displayed in Extended Data Figure 1. For the NIRCam F210M photometry, we do not repeat any data reduction procedures and adopt the  existing  extracted  light  curve 7 .  Extended  Data  Table  1  gives  an  overview  of  the JWST observations included in this work.", 'White Light Curve Analysis': "Seven separate white light curves were obtained from these JWST datasets: one from NIRSpec PRISM, two from NIRSpec G395H (one from each detector), two from NIRISS SOSS (one for each order), one from NIRCam F322W2, and one from the NIRCam F210M photometry. For the spectroscopic observations, white light curves are constructed using Eureka! 28 across  \nsimilar wavelength ranges to those adopted in the initial ERS publications 5,7,11,12 , except for NIRSpec PRISM, where we exclude wavelengths below 2 µm due to the presence of saturation. This corresponds to wavelength ranges of: 0.873-2.808 µm for NIRISS SOSS Order 1, 0.60.9 µm for NIRISS SOSS Order 2, 2.420-4.025 µm for NIRCam F322W2, 2.725-3.716 µm for NIRSpec G395H NRS1, 3.829-5.172 µm for NIRSpec G395H NRS2, and 2.0-5.5 µm for NIRSpec PRISM.  \nWe performed a joint fit to these light curves, in conjunction with six NGTS light curves, one TESS light  curve  (with  3  transits),  and  radial  velocity  measurements  from  CORALIE  and SOPHIE 14 ; these auxiliary datasets were selected as they were readily available, didn't show strong systematic effects and provided time-stamps we were able to join together with our JWST measurements.  Radial  velocities  are  mean-subtracted  before  performing  any  fitting. Dilution factors for each light curve are fixed to 1, implying we assume no dilution from nearby contaminants on those light curves. High-contrast imaging observations of WASP-39b reveal no nearby companions 41,42 , and no contaminating sources are apparent in the NIRCam target acquisition  image.  In  addition,  the  nominal  joint  fit  presented  and  used  in  this  work  had eccentricity fixed to 0 (a fit leaving the eccentricity as a free parameter with the priors described below constrains it at e < 0.039 with 99% credibility; the rest of the posterior parameters being consistent at 1-sigma with the ones here presented). All time-stamps are converted to BJD TDB.", 'The free parameters in this fit included:': "- -The period, which had a normal prior distribution centered at the value reported in Maciejewski et al. 2016 43 , i.e., 4.0552765 days but with a significantly larger standard deviation of 1 minute to allow for possible time-stamp mismatches between different BJD standards in the literature (e.g., UTC or TDB 44 ).\n- -The time of transit-center, which had a normal prior centered at 2459791.615201 BJD TDB (the time of the NIRSpec/G395H observations) with a relatively large standard deviation of 0.1 days.\n- -The impact parameter, also centered at the value reported in Maciejewski et al. 2016 43 , i.e., 0.45, but with a truncated normal distribution between 0 and 1, and with a larger standard deviation of 0.1.\n- -The stellar density, whose prior was set to a log-uniform distribution between 0.1 and 10 g/cm 3 .\n- -The radial velocity semi-amplitude, which had a uniform prior between 0 and 200 m/s.\n- -An individual radial velocity offset for each radial velocity instrument with data in Faedi et al. 2011 (CORALIE and SOPHIE), with uniform priors between -100 and 100 m/s as well as jitter terms with log-uniform priors between 1 and 100 m/s for each.\n- -An individual planet-to-star radius ratio for each light curve, which had a uniform prior between 0 and 0.3, in order to account for possible wavelength-dependent planet-tostar radius ratio changes.\n- -The  limb-darkening  coefficients  using  a  transformed  quadratic  law  via  the uninformative  sampling  prescription  of  Kipping  et  al.  2013  -  which  implied  two parameters  per  light  curve,  q 1 and  q 2 ,  with  uniform  distributions  between  0  and  1.\n- -A flux normalization term for each light curve, set with a normal prior centered at 0 and with a standard deviation of 100,000 ppm.\n- -A jitter term per light curve, set to a log-uniform prior between 0.1 and 10,000 ppm.  \nTo handle instrumental systematics in the light curves, based on analyses performed on the outof-transit data, we decided to use the following models:  \n- -A  Gaussian  Process  (GP)  on  the  NIRCam/F322W2,  NIRCam  F210M  photometry, NIRISS/SOSS Order 1 & 2 and TESS data. We chose a Matèrn 3/2 kernel and used time as the only regressor. The prior on the amplitude of this GP was set with a log-uniform distribution from 0.01 to 100 ppm for the JWST light curves and from 0.001 to 100 ppm for  the TESS light  curves.  The  timescale  also  had  a  log-uniform  prior  distribution between 0.01 and 100 days. The Bayesian evidence suggests that adding a GP to the NIRspec datasets does not provide an improvement to the fits.\n- -A linear model for the NIRSpec/G395H data with two regressors: a simple slope in time, and a regressor that was 0 before the tilt event observed in the data 12 and 1 after it.\n- -A linear model for NIRSpec/PRISM data with a simple slope in time.  \nAdding similar systematic models for the NGTS data didn't change the results of our fit. In total, 84 free parameters were used to fit a total of 12,206 data points, for which we used the dynamic  nested  sampling  scheme  as  implemented  in dynesty 45 .  Some  of  the  resulting parameters from this joint fit are presented in Table 1. Posteriors for select parameters are shown in Extended Data Figure 2.", 'Wavelength Binning Scheme for Spectrophotometric Light Curve Extraction': 'To extract the spectrophotometric light curves it is necessary to define a wavelength binning scheme for each dataset. The largest number of spectrophotometric bins, and therefore the highest  resolution,  is  reached  by  binning  at  the  native  pixel  resolution,  where  a spectrophotometric light curve is extracted for each individual pixel column. However, for these JWST modes, the native pixel resolution is higher than the native spectral resolution, which defines the difference in wavelengths, 𝛥 𝜆 , that can be resolved at a given wavelength, λ. As such, we adopt the native spectral resolution as a fundamental baseline for the extraction of the  spectrophotometric  channels.  While  higher  resolution  schemes,  including  native  pixel  \nresolution,  may  theoretically  offer  access  to  narrower  spectral  features,  understanding  the potential and reliability of such an approach is beyond the scope of this work.  \nThe  native  spectral  resolving  power,  which  defines  the  native  spectral  resolution,  can  be determined in units of pixels for each mode following 𝑅 pix =λ/ 𝐷𝑅 , where λ is the wavelength, 𝐷 is  the  dispersion  of  the  instrumental  mode,  and 𝑅 is  the  spectral  resolving  power  of  the instrumental mode. For all modes, we take the dispersion and resolving power curves from the reference data files provided by the JWST exposure time calculator, Pandeia 46 .  Importantly, 𝑅 pix is a continuous function of wavelength, whereas individual pixel columns have discrete edges and cannot be meaningfully subdivided in wavelength. Therefore, we convert 𝑅 pix to integer pixel values using a ceiling function to ensure that pixel columns are not split across two separate wavelength bins. We define the bin edges beginning with the lowest wavelength pixel column, where the wavelength at the lower edge of this column corresponds to the lower edge of the first spectrophotometric bin. Pixel columns are then added to this bin until the number of columns is equal to 𝑅 pix , and the wavelength at the upper edge of the final column corresponds to the upper edge of the first spectrophotometric bin. This process is repeated, using the previously determined upper edge as the starting lower edge for the next bin, until all bin edges have been defined. In the event that there is a transition in the integer value of 𝑅 pix as pixel columns are added to a bin, the highest value of 𝑅 pix is used to define the number of pixel columns that must be included. If there are not enough pixels available in the uppermost wavelength bin to satisfy this requirement, then those pixels are instead incorporated into the penultimate bin.  \nFor  each  instrumental  mode,  we  first  extract  spectrophotometric  light  curves  at  the  native spectral resolution following this binning scheme across a subset of the full wavelength range using  the  values  adopted  in  the  initial  ERS  publications  for  each  of  the  instrumental modes 5,7,11,12 .  This  corresponds  to  ranges  of:  0.873-2.808  µm  for  NIRISS  SOSS  Order  1, 0.630-0.853 µm for NIRISS SOSS Order 2, 2.420-4.025 µm for NIRCam F322W2, 2.7253.716 µm for NIRSpec G395H NRS1, 3.829-5.172 µm for NIRSpec G395H NRS2, and 0.5185.348 µm for NIRSpec PRISM. Additionally, for NIRISS SOSS we exclude ~100 columns that are impacted by zeroth-order contamination from background sources 5 . The effect of resolution on the measured spectrophotometric transit depths is explored further below.', 'Spectrophotometric Light Curve Fitting': 'Across all instrumental modes, we fit the spectrophotometric light curves using the Eureka! 28 package, which jointly fits both a systematic and astrophysical model component to each of the light curves. The systematic model consists of a first-order polynomial in time, whereas the astrophysical transit models are computed using the batman 47 package. We also fit a stepfunction to the NIRSpec G395H data to account for the flux drop close to mid-transit in the uncorrected light curves that is driven by a mirror tilt event 12 .  Orbital parameters are fixed during the fitting process using the values obtained from the white light curve fitting as shown in  Table  1.  Limb-darkening  is  incorporated  using  a  quadratic  law,  and  limb-darkening parameters are fixed in each of the light curve fits with initial values taken from the ExoTICLD package 48 using stellar parameters of [M/H]=0.0, 𝑇 eff =5512, and log( 𝑔 )=4.7. Fitting for the  limb-darkening  parameters  can  produce  wavelength-dependent  biases  as  a  function  of  \nwavelength binning resolution, and is investigated further below. The fitting itself is performed using MCMC as implemented by the emcee 49 package using 200 walkers, 1100 steps, and discarding the first 100 steps as a burn-in. Convergence is checked to ensure that the chains run for at least 50x the autocorrelation time.  \nThe transit depth precision, native spectral resolving power, and wavelength coverage resulting from the spectroscopic light curve fitting are displayed alongside similar properties for archival HST , VLT , and Spitzer data 16,17 for WASP-39b in Extended Data Figure 3. Of the used JWST observational  modes,  NIRSpec  PRISM  provides  the  best  transit-depth  precision  at  all wavelengths, at the expense of greatly reduced resolution. Where the wavelength ranges of the higher resolution modes overlap, the NIRCam F322W2 provides the best precision from ~2.42.9 µm at a slightly lower resolution, as well as providing unique access from ~3.7-3.8 µm where the NIRSpec G395H has no sensitivity due to the gap between the detectors that the spectrum falls across. The NIRISS SOSS and NIRSpec G395H modes have similar resolutions in the narrow region where they overlap, with the NIRISS SOSS providing superior precision below ~2.75 µm. NIRSpec G395H has a similar precision to NIRCam F332W2 from ~3.0-3.5 µm despite having a factor of ~2 higher resolution due to it also having a factor of ~2 higher throughput.  \nIt  is  clear  that JWST provides  a  dramatic  improvement  on  previous  capabilities  for  the characterization of transiting exoplanet atmospheres, offering increased wavelength coverage, resolution  and  precision.  The  NIRISS  SOSS  observations  provide  superior  resolution  to existing HST infrared  data  at  ~1-3  times  higher  transit-depth  precision,  and  the  NIRSpec PRISM offers a similar resolution at up to ~8 times higher precision. At longer wavelengths, all four instruments provide unrivaled advantages, and the Spitzer photometry is superseded by the spectroscopic capabilities of NIRSpec PRISM, NIRCam F332W2, and NIRSpec G395H. At native spectral resolution, the transit-depth precision of NIRCam F322W2 and NIRSpec G395H are a factor of ~2-3 times lower than Spitzer , but offer over two magnitudes improved resolving  power  compared  to  the Spitzer bandpasses.  Furthermore,  NIRSpec  PRISM,  in addition to the 𝑅 =100 NIRCam F332W2 and NIRSpec G395H datasets, offer both ~2-3 times higher  transit-depth  precision  and  ~20-40  times  improved  resolving  power.  Nevertheless, ground-based telescopes and HST remain uniquely capable of accessing shorter wavelengths <0.5-0.6 µm, a wavelength range that is crucial for capturing and measuring the presence and strength of aerosol scattering and metal absorption lines 50 .', 'Wavelength Binning Investigation and a Dependence on Limb-Darkening': 'We  also  investigate  and  extract  transit  spectra  at  coarser  wavelength  binning  schemes following the procedure above at two to five times lower than native spectral resolution for all modes, as well as a 𝑅 =100 binning scheme for the NIRISS SOSS, NIRCam F322W2, and NIRSpec  G395H  modes.  We  then  bin  the  original  native  spectral  resolution  transmission spectra to an approximately similar resolution as each lower resolution spectrum to explore the prevalence  and  extent  of  resolution-dependent  offsets.  This  is  performed  by  taking  the weighted mean of the native spectral resolution transit depths within each wavelength bin of the  lower  resolution  spectrum.  As  each  bin  of  the  native  spectral  resolution  spectrum constitutes  multiple  pixel  columns,  the  wavelength  ranges  of  the  binned  native  resolution  \ntransit spectrum can differ slightly from those binned to a lower resolution prior to the light curve fitting. However, the focus of this investigation is to identify broad deviations between different resolutions, and a more detailed examination will require future analysis at the native pixel resolution. Residuals from each comparison, both when fitting for or fixing the quadratic limb-darkening parameters, are shown in Extended Data Figure 4.  \nIn the case where the limb-darkening parameters are free parameters in the fitting process, we see  significant  differences  between  the  binned  native  resolution  spectra  and  those  that  are binned prior to the light curve fitting. Both NIRISS SOSS and NIRSpec G395H exhibit broad wavelength-dependent  offsets  that  become  more  pronounced  towards  lower  resolutions. Specifically,  at  R=100  NIRISS  SOSS has a mean difference of 32 ± 14 ppm (123 ± 58 ppm above 2.2 µm) and NIRSpec G395H has a mean difference of 110 ± 29 ppm (181 ± 51 ppm above 4.5 µm). The NIRCam F332W2 exhibits a broad uniform offset at all wavelengths, with a  mean  difference  of  58 ± 19 ppm at R=100. Conversely, NIRSpec PRISM exhibits a nonsignificant  mean  difference  of  3 ± 14  ppm  at  1/5  of  the  native  spectral  resolution.  We  reemphasise that the wavelength ranges of the binned native resolution transit spectrum can differ slightly from those binned to a lower resolution prior to the light curve fitting. This is a likely driver of any observed narrow offset features, which are not explored in this work.  \nIn  the  case  where  the  limb-darkening  parameters  are  fixed  during  the  fitting  process,  the agreements between the different resolution spectra are drastically improved. Non-significant mean transit depth differences are exhibited by NIRISS SOSS (3 ± 13 ppm, 8 ± 53 ppm above 2.2 µm), NIRCam F332W2 (13 ± 17 ppm), and NIRSpec PRISM (-3 ± 12 ppm). In contrast, a wavelength-dependent offset does remain for NIRSpec G395H, although its mean offset of 37 ± 27 ppm (87 ± 48 ppm above 4.5 µm) is still reduced compared to when fitting for the limbdarkening parameters.  \nThis  stark  difference  in  behavior  when  fitting  or  fixing  the  limb-darkening  parameters  is indicative of underlying biases of the adopted limb-darkening model. These biases appear to be strongest at regions of lower received detector counts, as evidenced by the offsets at the ends of the NIRSpec G395H / NIRISS SOSS data compared to the NIRCam F332W2 data (which has a relatively flat throughput and lower detector counts compared to other modes). Furthermore, these biases only seem to be evident for the higher resolution modes, and not the lower resolution NIRSpec PRISM mode. As the native spectral resolution light curves are at a lower  signal-to-noise  than  those  that  were  binned  to  lower  resolutions  prior  to  light  curve fitting, it is likely that they are more susceptible to biases introduced when fitting for the limbdarkening parameters. As these biases are not necessarily Gaussian in nature (i.e. may not be comparable to additional random noise), consistent results are not seen between the binned native resolution spectra and those that are binned prior to the light curve fitting.  \nFor future model analysis of these data, we recommend using the native spectral resolution spectrum for NIRSpec PRISM, the lower resolution R=100 spectra for the higher resolution modes, and in all cases the spectra that had their limb-darkening parameters fixed during the fitting process. We emphasize that this is not a global recommendation for all JWST datasets, but  one  that  is  specific  to  the  current  best  understanding  of  these  data.  Fixing  the  limb-  \ndarkening  parameters  has  provided  greater  agreement  across  different  resolution  binning schemes, however, the underlying reality is that these datasets are now uniformly biased by our limb-darkening assumptions. The extent of such biases is difficult to estimate at present, and significant  future  work  will  be  required  to  explore  the  impact  of  different  limb-darkening approaches on these data and those from other JWST observations.', 'Wavelength Overlap Comparison Between Instruments': "Each of the four instrumental modes has an overlap between its wavelength coverage and the wavelength coverage of the other three modes, allowing for a comparison between their relative measurements of the transit depth. The broadest comparison comes from the NIRSpec PRISM mode, of which the wavelength coverage completely encompasses the coverage of the other modes,  but  at  significantly  lower  resolution.  As  already  shown  in  Figure  2,  the  NIRSpec PRISM  data  exhibit  both  a  wavelength-independent  offset  across  all  wavelengths  and  a wavelength-dependent offset for data that experience saturation. Upon application of a -177 ppm  uniform  offset,  as  determined  from  the  model  analysis,  the  mean  offset  of  NIRSpec PRISM is -124 ± 6 ppm relative to NIRISS SOSS, 132 ± 13 ppm relative to NIRCam F322W2, and  17 ± 11  ppm  relative  to  NIRSpec  G395H.  Although  the  NIRISS  SOSS  is  in  better agreement  prior  to  offsetting  the  NIRSpec  PRISM  spectra,  its  wavelength  range  overlaps heavily with the saturated region of NIRSpec PRISM, which is not completely reliable. When looking at wavelengths unaffected by saturation, the mean offset of NIRSpec PRISM is -10 ± 27 ppm relative to NIRISS SOSS. There are more significant discrepancies at localized regions of the wavelength coverage. In some cases-for example, the deviation at 2.6 µm-the difference can  be  attributed  to  the  lower  resolution  of  PRISM  acting  to  'smooth  over'  atmospheric features that can be better captured at higher resolution.  \nEquivalent comparisons to Figure 2c for the smaller wavelength overlaps between the higher resolution NIRISS SOSS, NIRCam F322W2, and NIRSpec G395H modes are displayed in Extended Data Figure 5. We find an excellent agreement between NIRISS SOSS and NIRCam F322W2, with a non-significant mean offset of 11 ± 49 ppm, compared to 32 ± 46 ppm for the initial  data  release  spectra.  An  offset  is  still  present  between  NIRISS  SOSS  and  NIRSpec G395H of -372 ± 170 ppm, compared to -482 ± 132 ppm for the initial  data  release  spectra. However,  this  wavelength  range  is  at  the  edge  of  the  NIRSpec  G395H  and  may  be  more significantly affected by systematic effects due to low throughput. An offset is also present between  NIRCam  F322W2  and  NIRSpec  G395H  of  -138 ± 16  ppm,  but  this  is  greatly diminished compared to -343 ± 16 ppm for the initial data release spectra. Furthermore, the distribution of residuals for NIRCam F322W2 versus NIRSpec G395H is close to the expected normal distribution, but with a uniform offset (Extended Data Figure 5). Given the agreement between  NIRISS  SOSS  and  NIRCam  F322W2,  this  likely  suggests  that  a  wavelengthindependent bias remains in the NIRSpec G395H spectrum even after performing a joint white light curve analysis.  \nAll  offsets  as  measured  are  presented  in  Extended  Data  Table  2  for  ease  of  comparison. However, these values should not be interpreted as a generalizable property of the different detectors  between  the  different  instrumental  modes.  We  predict  that  these  offsets  will  be dependent on a currently unpredictable number of variables and will likely change between a  \ngiven  planet  and  observation.  Instead,  we  emphasize  that  although  offsets  between  JWST spectra have been identified, they can be mitigated through uniform light curve analysis.  \nIn  totality,  these  comparisons  demonstrate  that  the  joint  white  light  curve  analysis  has dramatically improved the agreement between these various JWST datasets. Nevertheless, this improvement  is  not  perfect,  and  some  offsets  do  remain  between  datasets.  Notably,  these offsets are only present relative to either the NIRSpec PRISM or NIRSpec G395H modes. This may  be  an  early  indication  of  an  uncorrected  systematic  or  bias  specific  to  the  NIRSpec instrument,  especially  considering  the  excellent  agreement  between  NIRISS  SOSS  and NIRCam F322W2. However, it is also possible that this agreement is a coincidence, and a firmer conclusion will require similar analyses across a wider range of JWST datasets. Where offsets  are  still  present  and  saturation  is  not  present,  they  appear  to  be  close  to  normally distributed.  Until  an  investigation  even  more  detailed  than  that  presented  in  this  work  is completed, the application of uniform offsets during model fitting and interpretation may be necessary.", 'NIRSpec PRISM Saturation': 'With a 2MASS 𝐽 magnitude of 10.66 14 , WASP-39 is above the brightness limit of the NIRSpec PRISM  mode  and  produces  detector  saturation  in  the  brightest  pixel  of  the  columns corresponding  to  ~0.63-2.06  µm.  As  the JWST detectors  make  use  of  non-destructive measurements to estimate the received flux (up-the-ramp sampling), if saturation occurs in a pixel after a large number of groups have been measured then the flux of that pixel may still be  reliably  measured  with  a  sufficient  number  of  unsaturated  groups.  However,  for  these NIRSpec PRISM observations there are only 5 groups per integration, with saturation occurring as early as the second group at the brightest part of the spectra. With so few measurements in each of these ramps, the ability to fit a slope and accurately estimate the flux for these pixels is diminished. When few groups are available, the linearity of the ramps is crucial to ensure an accurate determination of the count rates.  \nExtended Data Figure 6 demonstrates the differences between counts in neighboring groups as a diagnostic of the true linearity of the ramps. We see that the regions of the detector with higher count rates demonstrate a pattern that is indicative of an unexpectedly low count rate in the first group (2-1 is higher than 3-2, which suggests a similar effect to the first-group effect seen in Schlawin et al. 2023), or potentially an insufficient non-linearity correction (later group differences are lower than earlier group differences). Conversely, we see that rows 14 and 16 demonstrate a pattern that is indicative of a high count rate in the first group (2-1 is lower than 3-2), or potentially due to pixel cross-talk or charge diffusion as the central row approaches saturation in later groups (higher count differences in later groups than earlier groups). While we applied a more stringent saturation threshold in our updated PRISM analysis based on our analysis of the shapes of the ramps to avoid uncorrected non-linearity (approximately 70-75% rather than the 80% full-well threshold used in 11 ), future work should more closely explore the accuracy  of  the  NIRSpec  non-linearity  correction  with  data  that  saturate  more  slowly  and therefore contain significantly more groups to better determine the shape of the ramp and first group  impacts  on  partial  saturation  corrections.  In  particular,  it  would  be  useful  to  have multiple data sets of stars that saturate at different rates, in combination with a NIRISS SOSS  \nobservation, to fully characterize the impact of non-linearity, pixel cross-talk, and first group effects,  on  partially  saturated  exoplanet  transit  data.  Such  an  analysis  will  have  important implications on the full-well threshold that is appropriate to use when attempting to recover a partially saturated region on the NIRSpec detector and can help determine how and when cross talk occurs between neighboring pixels. We do note that regions of the detector that do not approach saturation in our data display flat group differences, which provides confidence in the extracted count rates in this region of the data.  \nThe analysis of the differences of neighboring groups makes it clear that the measured stellar flux rate is likely not representative of the true stellar flux rate in regions of the detector that rapidly approach saturation. The combination of the two regimes in Extended Data Figure 6 together shape the measured count rates in the saturated region of the spectrum. As more groups are added, the impact of inaccurate group counts becomes less significant; however, for the saturated region, we are limited in the number of groups available. In particular, where a low first group dominates and few groups are available, the measured ramps are steeper than reality, corresponding to a higher extracted flux and a diluted transit depth. As shown in Figure 2, this is  exactly  what  is  seen  in  the  saturated  region  of  the  offset  NIRSpec  PRISM  transmission spectrum as compared to the NIRISS SOSS spectrum. To correct this effect we perform an analysis of how adding groups impacts the extracted spectrum to estimate the amount of excess flux measured within the saturated region. These excess flux measurements correspond directly to a dilution correction we can apply to the NIRSpec PRISM saturated region.  \nTo explore how adding groups impacts the extracted spectrum, we reduce the data using the same  number  of  groups  across  the  entire  wavelength  range  while  masking  the  area  that becomes saturated in that number of groups. This corresponds to a total of 5 median stellar spectra using 1, 2, 3, 4, or 5 groups. Extended Data Figure 7 shows these spectra relative to the 5-group spectrum, which is equivalent to our standard extraction. We see a trend of increasing extracted  flux  within  the  saturated  region  (vertical  shaded  gray  regions,  where  the  darkest region saturates after 1 group, and the lightest region saturates after 4 groups) when using a fewer number of groups, suggesting that the first group effect is biasing the ramps when less than 5 groups are used. Extrapolating this trend by fitting a Gaussian, we can determine the median excess flux in regions of the spectrum that saturate in 2, 3, or 4 groups. We do not report a correction for the region that saturates in 1 group because the 1-group spectrum is very noisy and the first-group effect is not well-enough understood. For this reason, we suggest avoiding saturation after only 1 good group.  \nExtended Data Figure 8 shows the residuals after subtracting the NIRISS SOSS spectra from the  NIRSpec  PRISM  spectra  before  and  after  applying  the  dilution  corrections  shown  in Extended Data Figure 7c. We find that the median differences between SOSS and PRISM reduce from 0.4 𝜎 to 0.08 𝜎 . Though this is a marked improvement in agreement, it is important to note that the dilution corrections both assume that the shapes of the group differences are solely due to first-group effects and are extrapolated from a small number of groups, therefore suggesting that they may not be completely representative of the true effect. For this reason, and based on the extrapolated excess flux measurements, we suggest adopting a best practice of at least 5 groups before saturation is a safe regime in order to not be dominated by any of  \nthe effects that are impacting the group differences, and to ensure an accurate measurement of the stellar flux in this region.  \nWe further caution on relying on applying a similar correction to other NIRSpec PRISM data on  a  similarly  bright  star  without  NIRISS  SOSS  data  to  compare  to,  particularly  if  the wavelength regime that saturates in your NIRSpec PRISM data is crucial to your science. While the  broad  wavelength  coverage  of  NIRSpec  PRISM is unmatched, the results in this work demonstrate that (1) it can be offset from other modes, although in this work it may be partially due to the low total number of groups, and (2) the feature sizes in the saturated region are unreliable. Even with the application of a dilution correction, larger spectral differences are present relative to the NIRISS SOSS data than are observed for all other modes relative to NIRISS  SOSS.  Our  recommendations  are  to  avoid  partial  saturation  of  NIRSpec  PRISM, particularly if the saturated wavelengths are important to your science case, unless future JWST calibration  data  better  understands  the  first-group  effect  and/or  improves  the  non-linearity correction, therefore improving the ability to recover the saturated region. While the strategy of using multiple higher resolution modes would require the observation of a second transit, the higher spectral resolution may allow for additional science, such as the enhanced stellar modeling in Moran et al. 2023. NIRSpec PRISM remains a powerful tool for dimmer host stars to obtain wide wavelength coverage in an efficient single transit.', 'Modeling': 'To confirm the origin of the spectroscopic features present in the data from this synthesized release, we compare the observations against an atmospheric model of WASP-39b.  \nWe utilize all of the spectroscopic JWST data presented in this work, except for data in the  \nsaturated region of NIRSpec PRISM. For NIRISS SOSS, NIRCam F322W2, and NIRSpec G395H, we use the data binned to 𝑅 =100, and for NIRSpec PRISM we use the data at the native spectral resolution.  \nMotivated by the atmospheric inferences from the initial data releases (NIRISS: 10-30 × solar metallicity,  sub-solar  C/O 5 ;  NIRSpec  G395H:  3-10 × solar  metallicity,  sub-solar  C/O 12 ; NIRCam: 1-100 × solar metallicity, sub-solar C/O 7 ; NIRSpec PRISM: ~10 × solar metallicity, sub-solar C/O 11 ) we choose a 10 × solar metallicity, sub-solar carbon-to-oxygen ratio of 0.35 atmospheric  composition  under  the  assumption  of  full  day-night  heat  redistribution.  The atmospheric  model  assumes  a  1-dimensional  atmosphere  under  radiative-convectivephotochemical equilibrium (1D-RCPE). Calculating the RCPE models corresponds to coupling a thermochemical solver with a kinetics solver as recently described in Bell et al. submitted. First, the model is computed using the ScCHIMERA radiative-convective equilibrium solver (RCE) introduced in Piskorz et al. 2018, with recent updates and implementations to JWST data from  the  initial  data  releases 5,11 .  Then,  the  photochemical-equilibrium  (PE)  calculation corresponding  to  the  atmospheric  chemical  state  arising  from  the  chemical  kinetics  due  to photochemistry  and  vertical  mixing  is  computed  using  the  VULCAN 51 tool,  following  the model description in Tsai et al. 2022. We iterate over the RCE and PE calculations to ensure that  the  temperature-pressure  profile  and  gas  mixing  ratios  do  not  change,  resulting  in  a ScCHIMERA-to-VULCAN-to-ScCHIMERA-to-VULCAN-to-ScCHIMERA computation chain.  \nWe then considered the presence of inhomogeneous clouds and hazes by fitting a power-law and gray cloud-deck parametric model to the observations assuming the resulting 1D-RCPE model. The parametric cloud/haze model fits for a vertically uniform gray cloud opacity, 𝜅 cld , and a power-law haze assuming a scaling law for its cross-section fitting for scattering slope, 𝛾 , and the scale, 𝑎 52 . Then, we allow for the presence of inhomogeneous cloud cover by using a linear combination of the cloudy/hazy model and the cloud-free model following the formalism in Line et al. 2016, with a cloud fraction 𝜙 . When fitting the cloud parameters to the 1D-RCPE model, we allow for an offset in transit depth for the NIRSpec PRISM observations relative to all other instruments with a uniform prior between ± 500 ppm. Additionally, we allow for a scaling to the planetary radius referenced to 1 bar pressure. The final transmission spectrum in Figure 3 corresponds to the post-processed 1D-RCPE model with the median cloud parameters of log( 𝜅 cld ) = -29.45, 𝛾 = 1.63, 𝑙𝑜𝑔 (𝑎) = 1.95, 𝜙 = 0.84, and a scaling of the planetary radius of 97%.  \nThe fit for cloud and haze properties suggests that a non-negligible offset must be applied to the NIRSpec PRISM observations to match the 1D-RCPE model. For this specific atmospheric composition, we find that a best-fit negative offset of 177 ppm is required to bring the data to the same transit depth level as the model. Comparing this model to the remaining data from other instruments (e.g., NIRISS SOSS O2) seems to suggest that additional offsets for each instrument and detector may be required. The presence of offsets in the data and their impact on the inferred atmospheric properties are explored in greater detail in the companion work of Welbanks et al.', 'Data Availability': 'The data used in this paper are associated with JWST program ERS 1366 (observations 1-4) and  are  available  from  the  Mikulski  Archive  for  Space  Telescopes  (https://mast.stsci.edu). Specific data products for the: time series spectra, white and spectroscopic light curve fits, transmission spectra, and model  spectrum,  can  be  accessed  via  Zenodo  (DOI: 10.5281/zenodo.10161743).', 'Code Availability': 'This publication made use of the following code software to analyze these data: NumPy 53 , matplotlib 54 , SciPy 55 , pandas 56,57 , Batman 47 , emcee 49 , Exotic-LD 48 , dynesty 45,58 , SpectRes 59 , juliet 31 , and Eureka! 28 .', 'Acknowledgements': "This work is based on observations made with the NASA/ESA/CSA JWST .  The  data  were obtained  from  the  Mikulski  Archive  for  Space  Telescopes  at  the  Space  Telescope  Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-03127 for JWST . These observations are associated with program JWST -ERS-01366. Support for program JWST -ERS-01366 was provided by NASA through a grant from the Space Telescope Science Institute. This work is based in part on data collected under the NGTS project at the European Southern Observatory's La Silla Paranal Observatory. The NGTS facility is operated by a consortium of institutes with support from the UK Science  \nand Technology Facilities Council (STFC) under projects ST/M001962/1, ST/ S002642/1 and ST/W003163/1.", 'Author contributions': 'All  authors  played  a  significant  role  in  one  or  more  of  the  following:  development  of  the original proposal, management of the project, definition of the target list and observation plan, analysis  of  the  data,  theoretical  modeling,  and  preparation  of  this  paper.  Some  specific contributions are listed as follows. N.E.B., J.L.B., Z.K.B-T., I.J.M.C., J.M.D., L.K., M.R.L., D.K.S., K.B.S., and H.R.W provided overall program leadership and management. D.K.S., E.M.-R.K., H.R.W., I.J.M.C., J.L.B., K.B.S., L.K., M.L.-M., M.R.L., N.M.B., V.P. and Z.K.B.T. made significant contributions to the design of the program. K.B.S. generated the observing plan  with  input  from  the  team.  B.B.,  E.M.R.K.,  H.R.W.,  I.J.M.C.,  J.L.B.,  L.K.,  M.L.M., M.R.L., N.M.B. and Z.K.B.-T. led or co-led working groups and/or contributed to significant strategic  planning  efforts  such  as  the  design  and  implementation  of  the  prelaunch  Data Challenges.  A.L.C.,  D.K.S.,  E.S.,  N.E.,  N.P.G.,  and  V.P.  generated  simulated  data  for  pre launch  testing  of  methods.  A.L.C  and  E.M.M  co-led  the  data  synthesis  effort,  light  curve generation, spectroscopic  light-curve  fitting, limb-darkening  investigation,  saturation investigation, and production of the planetary spectrum. E.A., L.A, A.D.F., D.G., M.R., Z.R., and K.B.S. contributed significantly to the generation of, and provided, the spectral time-series that were used in this paper. N.E., R.B., R.O., G.M, and J.T. performed the white light-curve fitting. L.W. and M.L. performed the theoretical model generation for comparison with the data. A.L.C., E.M.M, N.E. and L.W. made significant contributions to the writing of this paper, A.L.C. and E.M.M. generated figures for this paper.', 'Competing interests': 'The authors declare no competing interests.', 'Correspondence and requests for materials': 'Should be addressed to A. L. Carter and/or E. M. May.', 'Author Information': "*Corresponding authors' emails aacarter@stsci.edu, erin.may@jhuapl.edu", 'Tables': 'Table 1: Best-fit orbital and instrumental parameters from white light curve fitting.', 'Extended Data Table 1: Overview of JWST Observations.': 'Observations are listed in the order they were acquired. For more detail on observational set up for these four modes, see 5,7,11 ; and 12 , respectively.', 'Extended Data Table 2: Offsets Between Spectra.': "All offsets should be read as the selected spectrum in the left hand column relative to a spectrum of a different column. It is critical to emphasize that these offsets are unique to the spectra presented in this paper, and should not be interpreted as inherent offsets between JWST's instrumental modes.", 'Figures': 'Figure  1:  White  light  curves  of  WASP-39b. All  data  are  presented  after  correcting  for systematics, including the G395H mirror tilt event 12 , with 1σ uncertainties. Solid lines indicate the best-fit model to each of the datasets from our joint fitting analysis, translucent points show the individual temporal measurements, and solid circles are the data after binning down to a lower temporal resolution (15 minutes for JWST datasets, 30 minutes for TESS/NGTS) .  The JWST light curve data are very precise and primarily lie underneath the best-fitting model lines. For the TESS/NGTS data, all light curves are phase-folded and the displayed best-fit model is an average across light curve fits. The residuals of the individual measurements compared to each best-fit model are displayed underneath each light curve.  \n<!-- image -->  \nFigure  2:  The  measured  transmission  spectrum  of  WASP-39b.  (a) The  measured transmission  spectra  for  all  four  instrumental  modes  as  reported  in  the  initial  data  release publications 5,7,11,12 (b) The measured transmission spectrum at native spectral resolution for NIRSpec PRISM, and at 1/5 of the native spectral resolution for the other modes (see Methods). (c) The residuals of the synthesized data for each mode relative to a linear interpolation of the NIRSpec PRISM data, with coloured shading indicating the 1σ uncertainty bounds. Horizontal lines indicate the median difference relative to the SOSS (solid), F322W2 (dashed), and G395H (dotted)  data.  See  Methods  for  a  detailed  quantitative  discussion  of  the  offsets  between individual modes. Regions where the NIRSpec PRISM data experience saturation are marked in gray shading, corresponding to saturation after one (darkest) to four (lightest) groups. All displayed uncertainties correspond to 1σ.  \n<!-- image -->  \nFigure  3:  Data  comparison  against  a  1D  RCPE  model  and  residuals  for  the JWST spectrum  of  WASP-39b.  (a,b) The JWST transmission  spectrum  of  WASP-39b  with  1σ uncertainties  (black  hexagons),  split  at  ~2.0  µm  for  clarity,  alongside  archival  data  (gray hexagons). For PRISM the data is at the native spectral resolution and excludes data in the saturated region; for the other modes the data is at 𝑅 =100. The 10 × solar metallicity, C/O=0.35 model is displayed in purple. (c) The residuals of the data for each mode relative to the best-fit model in addition to their 1σ uncertainties (shaded regions). (d) The probability density of the residuals for each mode relative to the best-fit model.  \n<!-- image -->', 'Extended Data Figures': 'Extended Data Figure 1: Median out-of-transit stellar spectra for WASP-39 in units of photoelectrons recorded at the detector. All datasets have been binned in wavelength to match the NIRSpec PRISM data. Residual differences between datasets are a result of the different instrumental throughputs.  \n<!-- image -->  \nExtended Data Figure 2: Best-fit orbital parameters for WASP-39b (a-e) and the radial velocity measurements (f). The measured transit depth varies between instrumental modes as they each span a different wavelength range. The best-fitting model to the radial velocity data (solid line) alongside its 1, 2, and 3 𝜎 contours (shaded regions) are also indicated.  \n<!-- image -->  \nExtended Data Figure 3: The achieved transit depth precision (a) and resolving power (b) across all datasets. For the transit depth uncertainty, solid lines correspond to the native spectral resolution datasets, and dashed lines correspond to the 𝑅 =100 datasets. Archival HST , VLT, and Spitzer data  are  also  displayed  (gray  lines  and  crosses).  As  the  reported  archival transit depths are an averaged combination of multiple transits, we inflate the reported transit depth uncertainties by a factor of √𝑁 , where 𝑁 is the number of transits for a given bandpass, to more accurately compare the signal-to-noise provided by a single transit across different instruments.  \n<!-- image -->  \nExtended Data Figure 4: Comparison between the transmission spectra under different binning schemes and limb-darkening approaches for each instrumental mode. Each panel  \n<!-- image -->  \nshows the residual when comparing the spectroscopic transit depths as determined by binning before  light  curve  fitting  is  performed  versus  those  determined  by  directly  binning  the measured transmission spectrum at the native spectral resolution. The width of each shaded region corresponds to the 1 𝜎 uncertainty in the measured residual. Dashed lines indicate zero deviation.  \nExtended  Data  Figure  5:  Comparison  of JWST instrumental  modes  across  their overlapping wavelength regions. Due to differences in wavelength binning, the minuends are interpolated to the wavelength grid of the subtrahends. Solid lines indicate the residual between the measured transit depths of the indicated instrumental modes at their native resolution, and the dashed line is the median difference across all wavelengths. A similar comparison relative to NIRSpec PRISM mode is displayed in Figure 2c. The probability density of the residuals is displayed next to each comparison, alongside a Gaussian distribution with zero mean and a standard deviation equal to the median error on the residual (gray shading).  \n<!-- image -->  \nExtended Data Figure 6: Differences in counts between neighboring groups after the non-  \n<!-- image -->  \nlinearity correction. A linear ramp would show a flat line difference between neighboring groups. Residual slopes in differences between neighboring groups are indicative of detector effects that are not fully corrected (denoted by the dashed lines connecting the median of group pairs). Above we show the distributions in differences between neighboring groups for three detector rows where row 15 corresponds to the central location of the trace, and rows 14 and 16 are the neighboring detector rows. Distributions are colored by the number of groups that are usable before reaching our saturation threshold. Note that we follow the same methodology as 11 in flagging an entire column as saturated when the central row saturates.  \nExtended Data Figure 7: Analysis of excess flux relative to the nominal 5-group spectrum.  \n<!-- image -->  \nThe 5-group spectrum uses a varying number of groups within the saturated region, as is done in  the  standard  PRISM extraction. (a) Extracted  spectra  using  different  numbers  of  groups relative to the 5-group spectrum. Data are not shown in regions that are already saturated, or where the 5-group spectrum uses the same number of groups. (b) Zoomed-in view of the Top Left panel, specifically the region in panel a that is enclosed in a box, excluding the noisy 1group spectrum. A Gaussian is fit to the excess flux spectrum. (c) The dilution corrections derived from the median excess flux fit. In all panels the vertical-gray shaded regions denote the level of saturation, with the darkest corresponding to saturation after 1 group and the lightest corresponding to saturation after 4 groups.  \n<!-- image -->  \nExtended Data Figure 8: Comparison of NIRISS SOSS to NIRSpec PRISM before and after the estimated dilution correction. Shaded regions indicate the wavelength ranges that experience saturation after the number of groups indicated in bold. A dilution correction cannot be computed for the region that experiences saturation after one group (dotted lines); however, we  show  an  example  correction  using  the  correction  value  for  the  regions  experiencing saturation after two groups. The probability density of the residuals is displayed in the right panel, alongside a Gaussian distribution with zero mean and a standard deviation equal to the median error on the residual (gray shading). 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