The identification of sources driving cosmic reionization a major phase transition from neutral hydrogen to ionized plasma around 600800 Myr after the Big BangSUP13SUP has been a matter of debateSUP4SUP. Some models suggest that high ionizing emissivity and escape fractions fSUBescSUB from quasars support their role in driving cosmic reionizationSUP56SUP. Others propose that the high fSUBescSUB values from bright galaxies generate sufficient ionizing radiation to drive this processSUP7SUP. Finally a few studies suggest that the number density of faint galaxies when combined with a stellarmassdependent model of ionizing efficiency and fSUBescSUB can effectively dominate cosmic reionizationSUP89SUP. However so far comprehensive spectroscopic studies of lowmass galaxies have not been done because of their extreme faintness. Here we report an analysis of eight ultrafaint galaxies in a very small field during the epoch of reionization with absolute magnitudes between MSUBUVSUB  17 mag and 15 mag down to 0.005LSUPSUP refs. SUP1011SUP. We find that faint galaxies during the first thousand million years of the Universe produce ionizing photons with logSUBionSUB Hz ergSUP1SUP  25.80  0.14 a factor of 4 higher than commonly assumed valuesSUP12SUP. If this field is representative of the largescale distribution of faint galaxies the rate of ionizing photons exceeds that needed for reionization even for escape fractions of the order of 5.
['Astrophysics - Astrophysics of Galaxies']
Most of the photons that reionized the Universe came from dwarf galaxies
{'Most of the photons that reionized the Universe came from dwarf galaxies': "Hakim Atek 1* , Ivo Labbé 2 , Lukas J. Furtak 3 , Iryna Chemerynska 1 , Seĳi Fujimoto 4 , David J. Setton 5 , Tim B. Miller 6 , Pascal Oesch 7,8 , Rachel Bezanson 5 , Sedona H. Price 5 , Pratika Dayal 9 , Adi Zitrin 2 , Vasily Kokorev 9 , John R. Weaver 10 , Gabriel Brammer 8 , Pieter van Dokkum, Christina C. Williams 12,13 , Sam E. Cutler 10 , Robert Feldmann 15 , Yoshinobu Fudamoto 16,17 , Jenny E. Greene 14 , Joel Leja 18,19,20 , Michael V. Maseda 21 , Adam Muzzin, Richard Pan 23 , Casey Papovich 24,25 , Erica J. Nelson 26 , Themiya Nanayakkara 2 , Daniel P. Stark 27 , Mauro Stefanon 28 , Katherine A. Suess 29,30 , Bingjie Wang 18,19,20 and Katherine E. Whitaker 8,10  \nInstitut d'Astrophysique de Paris, CNRS, Sorbonne Université, 98bis  \n1 Boulevard Arago, 75014, Paris, France.  \n- 2 Centre for Astrophysics and Supercomputing, Swinburne University of Technology, Melbourne, VIC 3122, Australia.\n- 3 Physics Department, Ben-Gurion University of the Negev, P.O. Box 653, Be'er-Sheva 84105, Israel.  \n4 Department of Astronomy, The University of Texas at Austin, Austin, TX 78712, USA.  \n5 Department of Physics and Astronomy and PITT PACC, University of Pittsburgh, Pittsburgh, PA 15260, USA.  \n6  \nCenter for Interdisciplinary Exploration and Research in Astrophysics  \n(CIERA) and Department of Physics & Astronomy, Northwestern  \nUniversity, IL 60201, USA.  \n- 7 Department of Astronomy, University of Geneva, Chemin Pegasi 51, 1290 Versoix, Switzerland.  \n8  \nCosmic Dawn Center (DAWN), Niels Bohr Institute, University of  \nCopenhagen, Jagtvej 128, København N, DK-2200, Denmark.  \n- 9 Kapteyn Astronomical Institute, University of Groningen, P.O. Box 800, 9700 AV Groningen, The Netherlands.  \n29 Department of Astronomy and Astrophysics, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064 USA. 30 Kavli Institute for Particle Astrophysics and Cosmology and Department of Physics, Stanford University, Stanford, CA 94305, USA.  \nThe identification of sources driving cosmic reionization, a major phase transition from neutral Hydrogen to ionized plasma around 600-800 Myr after the Big Bang, 1, 2, 3 has been a matter of intense debate. 4 Some models suggest that high ionizing emissivity and escape fractions ( 𝑓 esc ) from quasars support their role in driving cosmic reionization. 8, 9 Others propose that the high 𝑓 esc values from bright galaxies generates sufficient ionizing radiation to drive this process. 5 Finally, a few studies suggest that the number density of faint galaxies, when combined with a stellar-mass-dependent model of ionizing efficiency and 𝑓 esc can effectively dominate cosmic reionization. 6, 7 However, so far, low-mass galaxies have eluded comprehensive spectroscopic studies owing to their extreme faintness. Here we report an analysis of eight ultra-faint galaxies (in a very small field) during the epoch of reionization with absolute magnitudes between 𝑀 UV ∼ -17 to -15 mag (down to 0.005 𝐿 ★ 10,11 ). We find that faint galaxies during the Universe's first billion years produce ionizing photons with log( 𝜉 ion / Hz erg -1 ) = 25 . 80 ± 0 . 14 , a factor of 4 higher than commonly assumed values. 22 If this field is representative of the large scale distribution of faint galaxies, the rate of ionizing photons exceeds that needed for reionization, even for escape fractions of order five per cent.  \nWe combine ultra-deep JWST imaging data with ancillary Hubble Space Telescope ( HST ) imaging of the gravitational lensing cluster Abell 2744 (A2744 hereafter) in order to photometrically select extremely faint galaxy candidates in the epoch of reionization. A crucial component of the present study is the use of strong gravitational lensing to amplify the intrinsically-faint flux of distant sources. An accurate estimate of the magnification factor is required to retrieve the intrinsic luminosity of sources. This step relies on a good knowledge of the total mass distribution in the galaxy cluster. Here we use the most recent lensing model ( v1.1 ) published for the UNCOVER survey. The magnification factors for our galaxy sample range from 𝜇 ∼ 2 to 𝜇 ∼ 27. The values are reported in Table 1, together with 1 𝜎 uncertainties. The second part of the UNCOVERprogram consists of ultra-deep follow-up spectroscopy with the NIRSpec instrument. We used the Multi-Shutter Assembly to obtain multi-object spectroscopy in 7 pointings, totaling an exposure time ranging from 2.7 to 17.4 hours. Figure 1 shows the position of these sources in the A2744 field with the associated regions of high magnification and the configuration of the NIRSpec slits. Simultaneous spectral fits to the continuum and the emission lines provide estimates of the spectroscopic", 'Dwarf galaxies reionized the Universe': 'Fig. 6 : Stellar population simultaneous fitting to the NIRSpec spectra and NIRCam photometry . Panel a: Two representative sources (IDs 18924 and 16155) are shown. The best-fit Bagpipes model (red curve) is plotted over the observed NIRSpec spectrum (black curve), together with the error spectrum (gray curve). The NIRCam photometric measurements are represented with black points with their associated 1-sigma uncertainties. Panel b: Posterior distribution function for the main physical properties of ID 16155. When relevant, the parameters are corrected for magnification. Panel c: Same as panel b, for source ID 18924.  \n<!-- image -->', 'References': '- 37 Oke, J. B. & Gunn, J. E. Secondary standard stars for absolute spectrophotometry. Astrophys. J. 266 , 713-717 (1983).\n- 38 Bezanson, R. et al. The JWST UNCOVER Treasury survey: Ultradeep NIRSpec and NIRCam ObserVations before the Epoch of Reionization. arXiv e-prints arXiv:2212.04026 (2022).\n- 39 Weaver, J. R. et al. The UNCOVER Survey: A first-look HST+JWST catalog of 50,000 galaxies near Abell 2744 and beyond. arXiv e-prints arXiv:2301.02671 (2023).\n- 40 Rieke, M. J. et al. Performance of NIRCam on JWST in Flight. PASP 135 , 028001 (2023).\n- 41 Brammer, G. Grizli: Grism redshift and line analysis software. Astrophysics Source Code Library, record ascl:1905.001 (2019).\n- 42 Lotz, J. M. et al. The Frontier Fields: Survey Design and Initial Results. Astrophys. J. 837 , 97 (2017).\n- 43 Steinhardt, C. L. et al. The BUFFALO HST Survey. Astrophys. J. Suppl. Ser. 247 , 64 (2020).\n- 44 Jakobsen, P. et al. The Near-Infrared Spectrograph (NIRSpec) on the James Webb Space Telescope. I. Overview of the instrument and its capabilities. Astron. Astrophys. 661 , A80 (2022).\n- 45 Ferruit, P. et al. The Near-Infrared Spectrograph (NIRSpec) on the James Webb Space Telescope. II. Multi-object spectroscopy (MOS). Astron. Astrophys. 661 ,', 'Methods': 'Throughout the paper, we use AB magnitudes 37 and a standard cosmology with H 0 = 70 km s -1 Mpc -1 , Ω Λ = 0 . 7, and Ω 𝑚 = 0 . 3.', 'Observations and sample selection': 'The UNCOVER dataset consists of both imaging and spectroscopic observations of the lensing cluster A2744. The imaging observations and data reduction are described in detail in the survey and catalog papers. 38,39 Here, we briefly summarize the imaging and photometric products used in the present paper. HST imaging consists of 7 broadband filters (F435W, F606W, F814W, F105W, F125W, F140W, F160W). The NIRCam 40 images include short-wavelength (SW) broadband filters (F115W, F150W, F200W), long-wavelength (LW) broadbands (F277W, F356W, F444W), and one medium-band filter (F410M). Data were processed, and drizzled into 0.04 arcsec pix -1 mosaics using the Grism redshift and line analysis software for space-based spectroscopy ( G rizli; v1.6.0.dev99). 41 In terms of ancillary data, the HFF program 42 has obtained deep optical and NIR observations of the core area of A2744 with the Advanced Camera for Surveys (435W, F606W, F814W), and Wide-Field Camera Three (F105W, F125W, F140W, F160W). A wider area around the cluster has also been covered by the BUFFALO program 43 in almost identical broadband filters (without F435W and F140W). All HST observations were drizzled to the same pixel scale and the same orientation as the NIRCam mosaics.  \nThe second part of the UNCOVER program consists of ultra-deep follow-up spectroscopy with the NIRSpec instrument. 44 Data were obtained between July 31st and August 2nd 2023. Observations use the Prism mode and the Multi-Shutter Assembly 45 of NIRSpec to observe more than 650 targets. In order to optimize background subtraction, each target has been observed with a 3-slitlet nodding strategy. Observations were split into 7 pointings, with important overlap at the center, providing total on-target exposure times ranging from ∼ 2 . 7 ℎ to ∼ 17 . 4 ℎ . The spectral resolution is wavelength-dependent and varies between 𝑅 ∼ 30 to 𝑅 ∼ 300 over the full wavelength range 𝜆 ∼ 0 . 6 -5 . 3 𝜇𝑚 . Data were reduced using the JWST/NIRSpec analysis software msaexp version 0.6.10. The processing is based on level 2 MAST products, using the CRDS context file jwst\\_1100.pmap . The software performs basic reduction steps, including flat-field, bias, 1/f noise and snowballs correction, wavelength and photometric calibrations of individual exposure frames. 46 The extraction of 1D spectra from individual exposures is operated on inverse-weighted stack of 2D spectrum in the dispersion direction, following an optimal extraction procedure. 47 Then the software fits a Gaussian profile along the cross-dispersion direction to define the 1D extraction aperture. Finally, we compute the final deep 1D spectrum by inversevariance stacking the individual spectra. In order to account for slit loss effects, we apply a wavelength- (broadband-) dependent correction factor to re-scale the 1D spectrum to the observed NIRCam aperture photometry. We show an example of the imaging and spectroscopic data in Figure 5. A clear Lyman-break at rest-frame wavelength 𝜆 rest = 1216 Å is observed, together with multiple strong emission lines, including H 𝛼 +[N/i.pc/i.pc], [O/i.pc/i.pc/i.pc] 𝜆𝜆 4960 , 5008, H 𝛽 , H 𝛾 , and [O/i.pc/i.pc] 𝜆 3727  \nThe selection of our sample combines several criteria to constrain the photometric redshifts of the sources. First, we applied a color-color selection, based on a fluxdropout in the HST optical filters caused by rest-frame Lyman -𝛼 absorption by residual intergalactic Hydrogen gas. This selection consolidates most of the sources identified in the Hubble Frontier Fields (HFF) data 26,48 at 6 < 𝑧 < 9. Second, we performed spectral energy distribution (SED) fitting with the Eazy 49 software to estimate photometric redshifts, assuming a flat luminosity prior and the corr\\_sfhz library of stellar population templates. The allowed redshift range was set to 0 . 01 < 𝑧 < 20. The sources have been selected to have best-fit photometric solutions lying 6 < 𝑧 < 9 at the heart of the epoch of reionization. The final selection was then performed according to the intrinsic luminosity, combining high magnifications ( 𝜇 ≳ 2) and faint observed luminosities in F150W, resulting in intrinsic absolute UV magnitudes of order 𝑀 UV ≳ -17.', 'Spectral fitting': 'In order to determine the spectroscopic redshift of the sources, we fit spectral templates using msaexp , which based on the SED fitting software Eazy . 50 The code combine a set of templates to fit simultaneously the continuum, including the Lyman break caused by the IGM absorption, and the emission lines. For our analysis, we adopt the corr\\_sfhz\\_13 template library which include redshift-dependent SFHs, which are known to perform better than the default fsps\\_full library in recovering the true redshift. 39 Wesearch for the best-fit solution over the redshift interval 0 < 𝑧 < 15. The spectroscopic redshifts are reported in Table 2. Once the best-fit redshift is found, we refit the spectra, fixing the redshift to 𝑧 spec , with a set of spline functions to measure the continuum and Gaussians in order to measure the emission line fluxes. Examples of the best-fit model plotted over the observed spectrum are presented in Figure 1.', 'Strong lensing': "We use version v1.1 of the UNCOVER lensing model, 12 which is publicly available in the latest UNCOVER data release DR-1. The model is based on the parametric approach by Zitrin et al. 51 which has been re-written to be fully analytic, i.e. not limited by a grid-resolution. 52,12 The UNCOVER lens model of A2744 was constructed on a wealth of ground- and space-based data, including deep HST and JWST imaging, and Multi Unit Spectroscopic Explorer 53 (MUSE) spectroscopic redshifts of both cluster members and multiple images. 54,55,17,56 It comprises 421 cluster member galaxies identified in the ∼ 45 arcmin 2 UNCOVER field-of-view and five smooth cluster-scale dark matter (DM) halos. The model is constrained with 141 multiple images belonging to 48 sources and achieves an image reproduction RMS of Δ RMS = 0 . 51 '' in the lens plane. Thanks to the massive cluster substructures identified with UNCOVER, 12 the critical area of the cluster is 1 . 5 × larger than inferred from HFF data and the total source plane area with 𝜇 > 4 of ∼ 4 arcmin 2 for a source at redshift 𝑧 s = 6. The model uncertainties on the amplification values are derived from a Markov Chain Monte Carlo (MCMC) procedure within the modeling code Zitrin-analytic . 57,58 The temperatures of the MCMC are chosen to reflect typical", '16 Dwarf galaxies reionized the Universe': 'Fig. 5 : UNCOVER JWST data for galaxy 16155 at 𝑧 spec = 6 . 88. The top panels show image cutouts in seven different filters at increasing wavelength including ancillary HST/ACS data in F814W, and UNCOVER JWST imaging in F115W, F150W, F200W, F277W, F356W, and F444W bands (left to right). The central panel shows the UNCOVER NIRSpec data, with the 2D spectrum on top of the 1D optimally extracted spectrum (black with gray 1𝜎 uncertainty ranges). The red lines show the best-fit msaexp template spectrum. The observed-frame wavelengths of key emission lines are indicated as vertical dashed lines. The bottom panels show a zoomed in version of three different parts of the spectrum around the Ly 𝛼 break (left), around the [O/i.pc/i.pc/i.pc]+H 𝛽 emission lines (middle) and the H 𝛼 line (right).  \n<!-- image -->  \nsystematics inherent to parametric lens modeling techniques. 58 In order to better estimate systematics uncertainties inherent to models, we used an independent mass model for A2744 17 for comparison. The magnification factors are in good agreement within 1𝜎 uncertainties, except for two objects that have a difference at the level of 1.5 and 2𝜎 . Moreover, the statistical uncertainties derived from each model are of  \nthe same order. We have incorporated these systematic uncertainties in the quoted errors on the amplification factors.', 'Bagpipes': 'We infer global physical properties from SED-fitting using the Bagpipes software package 59,60 ). Before fitting, all models are convolved with the NIRSpec/Prism instrumental resolution curve provided by the Space Telescope Science Institute ( jwst\\_nirspec\\_Prism\\_disp.fits ), assuming that the flight performance is 1.3 times better than stated, and is consistent with an earlier work where a factor 1/0.7 is introduced for modeling 𝑧 > 10 galaxies. 61 Additionally, we fit with a wavelengthindependent velocity smoothing (0 < log ( 𝑣 smooth ) < 3 . 3) as a nuisance parameter. We adapt the following model grid: 62 stellar population models, the MILES spectral library, 63,64 CLOUDY nebular emission models, 65 and 66 dust model (with 0 < 𝐴 𝑣 < 5 and 0 . 3 < 𝑛 < 2 . 5 as free parameters). The stellar and gas phase metallicity are tied to the same value, and also included as a free parameter in the range -2 < log ( Z / Z ⊙ ) < 0 . 3. The ionization parameter is also left free in the range of -3 . 5 < log ( U ) < -1 . 0. We parameterize the star formation history as a delayed𝜏 model (SFR ∝ -𝑡 / 𝜏 , which can flexibly produce rising or falling star formation histories for this range of 𝜏 at the redshift of the sample), with the age ( -3 < log(age) < 0 . 48) and 𝜏 (0.01 < 𝜏 <5) as free parameters. This parameterization has been shown to reliably recover star formation rates and the mass formed in recent star formation, but is potentially susceptible to under-estimating stellar masses due to outshining by the youngest stellar population. 67 Redshift is restricted to vary in a narrow range around the best-fitting spectroscopic redshifts ( ± 0.1). We fit for a polynomial calibration vector of order 2 after applying a wavelength-independent calibration to scale the normalization of the spectrum to the photometry. The Bagpipes white noise model is used to allow for underestimated errors up to a factor of 10. A signal-to-noise ceiling of 20 is imposed on both our photometry and spectroscopy to account for systematic issues with the flux calibration. Sampling is performed via PyMultinest , 68,69 with the default Bagpipes convergence criteria. The most important physical properties derived from this procedure are presented in Table 2, and example fits and posteriors are presented in Figure 6.  \nIn addition to the star-formation rate (SFR) derived from SED-fitting, we also compute the SFR based on H 𝛼 or H 𝛽 recombination line. The H 𝛼 indicator traces massive short-lived stars on a timescale of a few Myr, whereas the UV emission indicates an SFRaveraged on a longer timescale up to a few 100 Myr. We report high values for the SFR(H 𝛼 )/SFR(UV) ratio in the range [5-60], indicating recent bursts of star-formation in these young systems. This is in line with their specific star-formation rates (sSFR). Given their low stellar masses, these sources have log(sSFR(H 𝛼 ) / yr -1 )=[ -7 . 4 , -6 . 2], which means that they can double their stellar mass within 2 to 20 Myr.', 'BEAGLE': 'Werun an additional spectral fit with the BayEsian Analysis of GaLaxy sEds 70 tool ( BEAGLE ) on the magnification-corrected spectra. BEAGLE uses the latest version of the Bruzual & Charlot stellar population synthesis models 71 and nebular emission templates computed with CLOUDY . 72,73 We then assume a Chabrier 74 initial stellar mass function (IMF), an SMC dust attenuation law, 75 the latest Inoue et al. analytic  \nIGM attenuation models, 32 and a delayed exponential SFH as for our Bagpipes fit. All other parameters are left free to vary with uniform or log-uniform priors: stellar mass log ( 𝑀 / M ⊙ ) ∈ [ 4 , 10 ] , current (10 Myr) SFR log ( 𝜓 / M ⊙ yr -1 ) ∈ [-2 , 4 ] , maximum stellar age log ( 𝑡 age / yr ) ∈ [ 6 , 𝑡 universe ] , star-formation e-folding time log ( 𝜏 / yr ) ∈ [ 5 . 5 , 9 . 5 ] , stellar metallicity log ( 𝑍 / Z ⊙ ) ∈ [-2 . 2 , -0 . 3 ] , effective V -band dust attenuation optical depth ˆ 𝜏 𝑉 ∈ [ 0 , 3 ] , effective galaxy-wide ionization parameter log 𝑈 ∈ [-4 , -1 ] , gas-phase metallicity log ( 𝑍 gas / Z ⊙ ) ∈ [-2 . 2 , -0 . 3 ] , and dust-to-metal mass ratio 𝜉 d ∈ [ 0 . 1 , 0 . 5 ] . The posterior distribution of the physical properties that we derive with BEAGLE agree well with the Bagpipes results presented in Table 2.', 'Contribution of galaxies to reionization': 'Using the present spectroscopically-confirmed sample of ultra-faint galaxies, we have the opportunity to put constraints on the UV luminosity function. We first describe the selection procedure of our sample and associated biases. The original sample has been selected in HFF studies, 26,48 based on HST observation. The selection of the original photometric sample is based on Lyman break criteria, which identify the dropout due to continuum absorption by the neutral IGM blueward of Ly 𝛼 . The photometric redshift through SED fitting were only measured to refine the redshift solution. In addition, three sources were selected from the UNCOVER imaging data based on their photometric redshifts. Regarding these three sources, strong emission lines tend to help put stronger constraints on the photometric redshift estimates, resulting in narrower best-fit solutions, which could favor strong-line emitters in the sample selection. 76 For the selection of this spectroscopic sample, we primarily focus on faint intrinsic magnitudes, typically 𝑀 UV ≳ -17 mag, as can be seen in Figure 2. Their apparent magnitude ranges from 𝑚 𝐹 150 𝑊 = 27 . 4 to 29.7 AB mag. While there is an intentional bias to select intrinsically faint galaxies in this study, this is less the case regarding observed magnitudes. Finally, during the Multi-Shutter Assembly design, we assigned equal weights to all galaxies. Therefore, the only bias introduced here is the optimization of the number of sources that are included in one mask configuration. Therefore, galaxies that did not make it to the final sample were simply excluded for mask optimization reasons.  \nFirst, we compute an initial UV LF based on the present sample binned in four magnitude bins and a survey volume, which depends on the original selection of the source. For the five HFF sources, we use the source plane effective volume 26 as a function of the magnitude bin. We rebin the original HFF sample to match the new magnitude bins. A scaling factor is then applied to the LF points to match the HFF completeness-corrected counts. Finally, we apply a correction factor based on the success rate of the spectroscopic confirmation. For the three sources outside the HFF area, we recompute the source plane effective survey volume using the new lensing model and assuming a similar completeness function across the field. We perform the same exercise of rescaling the number counts in each magnitude bin. The final UV LF is calculated by combining all galaxies with their associated corrected effective volume. Regarding uncertainties, we use the HFF volume uncertainties for the HFF sources, and the updated volume uncertainties for the new sample, respectively. We  \nFig. 7 : Spectroscopic constraints on the UV luminosity function . The UV luminosity function as determined from our spectroscopic sample is represented by orange points. Also shown, the photometric determination from the HFF data, 26 together with the best-fit Schechter function (blue curve) and a modified Schechter with a potential turnover (teal curve). The shaded region of each curve represent the 1 -𝜎 uncertainties  \n<!-- image -->  \nnote that the HFF uncertainties include systematic effects derived from a comparison between four independent models. 26 For the three sources outside the HFF coverage, the comparison to another independent model 17 shows that the uncertainties are negligible for such small amplification factors. We also include Poisson errors and cosmic variance in the final LF results. For the survey volume probed by our program, we estimated cosmic variance to be around 𝜎 CV ∼ 30%. 77 Overall, In Figure 7, we show that our measurements (orange points) are in good agreement with the faint-end of the photometric UV LF derived from HFF observations 26 (gray points).  \nIn our efforts to determine whether galaxies can reionize the Universe, we proceed to calculate another crucial parameter: the production efficiency of ionizing radiation 𝜉 ion . This quantity is defined as the ratio between the LyC photon production rate in the units of s -1 , and the observed non-ionizing UV luminosity density 𝐿 UV estimated at 1500 Å in units of erg s -1 Hz -1 :  \n𝜉 ion = 𝑁 ( 𝐻 0 ) 𝐿 𝑈𝑉 [ 𝑒𝑟𝑔 -1 𝐻𝑧 ] , (1)  \nwhere 𝑁 ( 𝐻 0 ) can be estimated from the H 𝛼 Balmer line 78 assuming a case B recombination theory: 79  \n𝐿 ( 𝐻𝛼 ) [ 𝑒𝑟𝑔 𝑠 -1 ] = 1 . 36 × ( 1 -𝑓 𝑒𝑠𝑐 ) 10 -12 𝑁 ( 𝐻 0 ) [ 𝑠 -1 ] (2)  \nwhere 𝐿 ( 𝐻𝛼 ) is in units of erg s -1 , and 𝑓 esc is the escape fraction of Lyman continuum radiation. In this calculation, we assume that 𝑓 esc =0, meaning that all Lyc photons are reprocessed into the Balmer lines. The derived 𝜉 ion value can be considered as a lower limit, since higher 𝑓 esc will lead to a higher 𝜉 ion . The H 𝛼 emission line is not detected in source ID 18924. In this case, we use the H 𝛽 luminosity and a case B conversion factor. Our measurements are reported in Figure 3, together with literature results. 80,81,82,83,84,85 These uncertainties due to potential field-to-field variations can also affect the ionizing properties of galaxies. We incorporated cosmic variance errors ( 𝜎 UV ∼ 30%) to the 𝜉 ion values for both 𝑀 UV > -16 . 5 mag and 𝑀 UV < -16 . 5 mag (cf. Figure 3).  \nSpectroscopic measurements in brighter galaxies have also reported 𝜉 ion values higher than canonical values. 88 Several JWST studies have measured 𝜉 ion in faint galaxies at the epoch of reionization. 86,23 However, their results are based on emission line fluxes inferred from broadband excess, rather than direct spectroscopic measurements. In particular, JWST medium-band photometric data have been used to infer 𝜉 ion for galaxies over the redshift range 3 < 𝑧 < 7. 87 These measurements offer an opportunity to explore a larger population of galaxies through wide-area imaging. They report ionizing efficiencies in the range log( 𝜉 ion / Hz erg -1 ) =25 . 31 -25 . 39, where galaxies with strong Ly 𝛼 emission tend to have the highest values. These values are smaller than our average measurements for the faintest galaxies. However, the vast majority of their sample is at significantly lower redshifts. Their redshift distribution has two peaks at 𝑧 = 3 and 𝑧 = 5, and the median redshift of their sample is 𝑧 = 4 . 02, which is well below the epoch of reionization. Among their sample of 370 galaxies, only ∼ 25 galaxies lie within the epoch of reionization. Therefore, their average 𝜉 ion value is not representative of the epoch of reionization. Furthermore, this difference is smaller if we take into account the dynamical range of UV magnitudes explored in their study. Although they find a weak dependency of 𝜉 ion with 𝑀 UV , their sample consists of galaxies with UV magnitudes ranging from -23 to -15.5 mag, compared to our subsample of 𝑀 UV > -16 . 5 mag. While imaging-based measurements can be complementary to spectroscopic studies, they have larger uncertainties (in the range 𝜎 = 0.43 - 0.64), due to the way emission line fluxes are inferred, and are model-dependent, since the continuum is derived from SED-fitting.  \nFinally, we derive constraint on the LyC escape fraction using indirect indicators calibrated in a large sample of nearby LyC emitting galaxies. Since the LyC emission is impossible to measure at the epoch of reionization, large efforts have been devoted in the last two decades to determine the escape fraction in 𝑧 < 4 galaxies, and more importantly establish indirect methods, which can be transferable to reionization sources. This was precisely the motivation of the recent Low-redshift Lyman continuum survey (LzLCS). 89 Among the different physical properties of LyC leakers, the observed UV continuum slope 𝛽 has been identified as a promising proxy of 𝑓 esc . 28 Here, we use the UV slopes derived from the best-fit models of Bagpipes and  \nthe LzLCS relation to infer 𝑓 esc . The derived values for the present sample range from 4.5% to 15.6%. Despite large uncertainties (around 50%), only two sources have 𝑓 esc values that can reach below 4% at 1-sigma.  \nNow with all three properties in hand, we can assess the contribution of faint galaxies to cosmic reionization. We compute the ionizing photon emissivity of galaxies, which is the product of the total UV luminosity density 𝜌 UV , derived from integrating the UV LF, and the production efficiency 𝜉 ion . The result will depend on the faint integration limit, which is set the faintest bin of the spectroscopic LF at 𝑀 UV =-15 mag. By multiplying this quantity by the escape fraction 𝑓 esc , we obtain the total ionizing photon rate density that is available to ionize the IGM. Down to 𝑀 UV =-15 mag, modest 𝑓 esc values around 5% are sufficient to maintain reionization.  \nWe measure the gas-phase oxygen abundance using the strong optical lines diagnostic. Specifically, we use the R3=log([O/i.pc/i.pc/i.pc] 𝜆 5007 /H 𝛽 ) and adopt the most recent empirical calibrations at high-redshift. 90,91,92 All of our sources show detections of [O/i.pc/i.pc/i.pc] 𝜆 5007 and H 𝛽 . Furthermore, we also separate our sample into two bins according to the EW(H 𝛽 ) at EW(H 𝛽 )=100Å to account for ionization parameter variations. 90 Also, for a given value of R3, the calibration defines two metallicity solutions. Although these sources have likely low metallicities, we use the O32=log([O/i.pc/i.pc/i.pc] 𝜆𝜆 4949,5007/[O/i.pc/i.pc] 𝜆𝜆 3727,3729) ratio to distinguish between the two branches. For most of the sources, the [O/i.pc/i.pc] is not detected, which provides a lowerlimit on O32, which is found to vary in the range O32=[0.7-1.4]. Using the O32 metallicity indicator, the resulting values are all compatible with the low-metallicity branch solution. The metallicity measurements are reported in Table 2.  \nIn addition to the physical properties that may ease the escape of ionizing photons from these galaxies, metallicity can also inform us on the ionizing properties of this population. We measured the gas-phase metallicity using the R3 = [O/i.pc/i.pc/i.pc]/H 𝛽 line ratio based on the most recent calibrations. 93 We find extremely low metallicities, ranging from 12+log(O/H)= 6.70 to 7.46, which corresponds to 1% to 6% of the solar metallicity. Such low metallicities are often suggestive of strong ionizing radiation from massive stars. 94 At the same time, these distant low-mass galaxies, considered as the building-blocks of present-day galaxies, are expected to be metal-poor, owing to their supposedly pristine gas conditions. We note that our estimate relies on the calibration of strong lines diagnostics, which are prone to significant uncertainties at 𝑧 > 6. On average, these estimates lie with 1 to 2 𝜎 intervals from each other.', 'Size Measurements': "To further characterize these sources, we measure their sizes by fitting their morphology in the NIRCam F150W filter with a Sérsic profile. 95 Measurements of the half-light radii for all eight galaxies are performed using the pysersic 96 package. For each source, we assume a single Sersic profile and mask all nearby sources in the photometric catalog. 39 The priors for the half-light radius, Sersic index, axis ratio and position angle are all uniform and varied from 0 . 5 -10 pixels, 0 . 65 -4, 0 . 1 -1 and 0 -2 𝜋 respectively. Priors central position and flux are represented as Gaussian distributions with the location and width based on the photometric catalog. 39 A flat sky background is fit simultaneously. The Posterior distribution is explored using the  \nNo-U Turn (NUTS) 97 sampler implemented in numpyro 98 with 2 chains for 1,000 warm-up and sampling steps each.  \nSeveral of our objects are significantly distorted by the gravitational lensing which means that we need to take the shear into account when deriving the half-light radius. In order to do that, we use our lensing model to derive the tangential and radial magnifications, defined as 𝜇 = 𝜇 t 𝜇 r , from the deflection field at each source's position and redshift. Since our objects are sheared along the tangential direction, we use the tangential component of the magnification to correct the half-light radii.  \nThe derived effective radii, corrected for magnification and taking the shear into account, vary between 𝑟 eff = 30 to 300 pc. Overall, these constraints show that these sources are small, in broad agreement with an extrapolation to lower masses of the size-mass relation derived at similar redshifts, 99 albeit with significant scatter. Such small sizes and high sSFRs also supports the scenario of stochastic star formation histories in these systems or dust ejection, 100 owing to their small dynamical time and a low gravitational potential.", '24 Dwarf galaxies reionized the Universe': 'A81 (2022).  \n- 46 Heintz, K. E. et al. Extreme damped Lyman𝛼 absorption in young star-forming galaxies at 𝑧 = 9 -11. arXiv e-prints arXiv:2306.00647 (2023).\n- 47 Horne, K. An optimal extraction algorithm for CCD spectroscopy. 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Computing in Science and Engineering 9 , 90-95 (2007).  \n- 104 Brammer, G. msaexp: NIRSpec analyis tools. Zenodo (2022).\n- 105 Harris, C. R. et al. Array programming with NumPy. Nature 585 , 357-362 (2020).\n- 106 Hoffman, M. D. & Gelman, A. The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. arXiv e-prints arXiv:1111.4246 (2011).  \n107 Phan, D., Pradhan, N. & Jankowiak, M. Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro. arXiv e-prints arXiv:1912.11554 (2019).  \n108 Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods 17 , 261-272 (2020).  \nData Availability. The NIRCam and HST imaging data are available on the UNCOVER webpage: https://jwst-uncover.github.io/. The NIRSpec spectroscopic data are publicly available through the Mikulski Archive for Space Telescopes ( MAST ; https://archive.stsci.edu/), under program ID 2561. The UNCOVER Lensing products are available at https://jwst-uncover.github.io/DR1. html#LensingMaps.  \nCode Availability. Astropy, 101,102 Bagpipes, 59,60 BEAGLE, 70 EAzY, 50 Matplotlib, 103 msaexp v0.6.10, 104 NumPy, 105 NUTS, 106,107 PyMultinest, 68,69 pysersic, 96 SciPy, 108 GrizLi https://github.com/gbrammer/grizli  \nAcknowledgments. H.A. and IC acknowledge support from CNES, focused on the JWST mission, and the Programme National Cosmology and Galaxies (PNCG) of CNRS/INSU with INP and IN2P3, co-funded by CEA and CNES. H.A thanks the Cosmic Dawn Center (DAWN) for their support. DAWN is funded by the Danish National Research Foundation under grant No. 140. IL acknowledges support by the Australian Research Council through Future Fellowship FT220100798. P.D. acknowledges support from the NWO grant 016.VIDI.189.162 (\'ODIN") and from the European Commission\'s and University of Groningen\'s CO-FUND Rosalind Franklin program. A.Z. acknowledges support by Grant No. 2020750 from the United StatesIsrael Binational Science Foundation (BSF) and Grant No. 2109066 from the United  \nStates National Science Foundation (NSF), and by the Ministry of Science & Technology, Israel. The work of C.C.W. is supported by NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation.  \nAuthor Contributions. H.A. led the analysis and article writing. L.J.F. and A.Z. constructed the lens model and extracted lensing related quantities. S.F produced figures. I.L. and R.B. are the PIs of the UNCOVER program. R.B. and I.L. designed the observations and reduced the spectra. J.W. and B.W. produced the catalogs used for target selection. P.D. provided simulations to interpret the observational results obtained. V.K. produced line measurements. I.C. estimated survey volumes. D.J.S. ran SED fitting analysis. T.B.M. measured the galaxy sizes. All authors contributed to the manuscript and aided the analysis and interpretation.  \nAuthor Information. Correspondence and requests for materials should be addressed to hakim.atek@iap.fr. Reprints and permissions information is available at www.nature.com/reprints. The authors declare no competing interests.'}