We report the discovery of a candidate galaxy with a photo-z of z ∼ 12 in the first epoch of the James Webb Space Telescope (JWST) Cosmic Evolution Early Release Science Survey. Following conservative selection criteria, we identify a source with a robust z phot = 11.8 − 0.2 + 0.3 (1σ uncertainty) with m F200W = 27.3 and ≳7σ detections in five filters. The source is not detected at λ < 1.4 μm in deep imaging from both Hubble Space Telescope (HST) and JWST and has faint ∼3σ detections in JWST F150W and HST F160W, which signal a Lyα break near the red edge of both filters, implying z ∼ 12. This object (Maisie’s Galaxy) exhibits F115W − F200W > 1.9 mag (2σ lower limit) with a blue continuum slope, resulting in 99.6% of the photo-z probability distribution function favoring z > 11. All data-quality images show no artifacts at the candidate’s position, and independent analyses consistently find a strong preference for z > 11. Its colors are inconsistent with Galactic stars, and it is resolved (r h = 340 ± 14 pc). Maisie’s Galaxy has log M */M ⊙ ∼ 8.5 and is highly star-forming (log sSFR ∼ −8.2 yr−1), with a blue rest-UV color (β ∼ −2.5) indicating little dust, though not extremely low metallicity. While the presence of this source is in tension with most predictions, it agrees with empirical extrapolations assuming UV luminosity functions that smoothly decline with increasing redshift. Should follow-up spectroscopy validate this redshift, our universe was already aglow with galaxies less than 400 Myr after the Big Bang.

We introduce the Dense Basis method for Spectral Energy Distribution (SED) fitting. It accurately recovers traditional SED parameters, including M * , SFR and dust attenuation, and reveals previously inaccessible information about the number and duration of star formation episodes and the timing of stellar mass assembly, as well as uncertainties in these quantities. This is done using basis Star Formation Histories (SFHs) chosen by comparing the goodness-of-fit of mock galaxy SEDs to the goodness-of-reconstruction of their SFHs. We train and validate the method using a sample of realistic SFHs at z = 1 drawn from stochastic realisations, semi-analytic models, and a cosmological hydrodynamical galaxy formation simulation. The method is then applied to a sample of 1100 CANDELS GOODS-S galaxies at 1 < z < 1.5 to illustrate its capabilities at moderate S/N with 15 photometric bands. Of the six parametrizations of SFHs considered, we adopt linear-exponential, bessel-exponential, lognormal and gaussian SFHs and reject the traditional parametrizations of constant (Top-Hat) and exponential SFHs. We quantify the bias and scatter of each parametrization. 15% of galaxies in our CANDELS sample exhibit multiple episodes of star formation, with this fraction decreasing above M * > 10 9.5 M . About 40% of the CANDELS galaxies have SFHs whose maximum occurs at or near the epoch of observation. The Dense Basis method is scalable and offers a general approach to a broad class of data-science problems.

The star formation histories (SFHs) of galaxies contain imprints of the physical processes responsible for regulating star formation during galaxy growth and quenching. We improve the Dense Basis SFH reconstruction method of Iyer & Gawiser (2017), introducing a nonparametric description of the SFH based on the lookback times at which a galaxy assembles certain quantiles of its stellar mass. The method uses Gaussian Processes to create smooth SFHs that are independent of any functional form, with a flexible number of parameters that is adjusted to extract the maximum possible amount of SFH information from the SEDs being fit. We apply the method to reconstruct the SFHs of 48,791 galaxies with H < 25 at 0.5 < z < 3.0 across the five CANDELS fields. Using these SFHs, we study the evolution of galaxies as they grow more massive over cosmic time. We quantify the fraction of galaxies that show multiple major episodes of star formation, finding that the median time between two peaks of star formation is ∼ 0.42 +0.15 −0.10 t univ Gyr, where t univ is the age of the universe at a given redshift and remains roughly constant with stellar mass. Correlating SFHs with morphology, we find that studying the median SFHs of galaxies at 0.5 < z < 1.0 at the same mass (10 10 < M * < 10 10.5 M ) allows us to compare the timescales on which the SFHs decline for different morphological classifications, ranging from 0.60 −0.54 +1.54 Gyr for galaxies with spiral arms to 2.50 −1.50 +2.25 Gyr for spheroids. The Gaussian Process-based SFH description provides a general approach to reconstruct smooth, nonparametric SFH posteriors for galaxies with a flexible number of parameters that can be incorporated into Bayesian SED fitting codes to minimize the bias in estimating physical parameters due to SFH parametrization.

Many scientific investigations of photometric galaxy surveys require redshift estimates, whose uncertainty properties are best encapsulated by photometric redshift (photo-z) posterior probability density functions (PDFs). A plethora of photo-z PDF estimation methodologies abound, producing discrepant results with no consensus on a preferred approach. We present the results of a comprehensive experiment comparing twelve photo-z algorithms applied to mock data produced forLarge Synoptic Survey Telescope The Rubin Observatory Legacy Survey of Space and Time (lsst) Dark Energy Science Collaboration (desc). By supplying perfect prior information, in the form of the complete template library and a representative training set as inputs to each code, we demonstrate the impact of the assumptions underlying each technique on the output photo-z PDFs. In the absence of a notion of true, unbiased photo-z PDFs, we evaluate and interpret multiple metrics of the ensemble properties of the derived photo-z PDFs as well as traditional reductions to photo-z point estimates. We report systematic biases and overall over/under-breadth of the photo-z PDFs of many popular codes, which may indicate avenues for improvement in the algorithms or implementations. Furthermore, we raise attention to the limitations of established metrics for assessing photo-z PDF accuracy; though we identify the conditional density estimate (CDE) loss as a promising metric of photo-z PDF performance in the case where true redshifts are available but true photo-z PDFs are not, we emphasize the need for science-specific performance metrics.