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We report the discovery of 15 exceptionally luminous 10 ≲ z ≲ 14 candidate galaxies discovered in the first 0.28 deg2 of JWST/NIRCam imaging from the COSMOS-Web survey. These sources span rest-frame UV magnitudes of −20.5 > M UV > −22, and thus constitute the most intrinsically luminous z ≳ 10 candidates identified by JWST to date. Selected via NIRCam imaging, deep ground-based observations corroborate their detection and help significantly constrain their photometric redshifts. We analyze their spectral energy distributions using multiple open-source codes and evaluate the probability of low-redshift solutions; we conclude that 12/15 (80%) are likely genuine z ≳ 10 sources and 3/15 (20%) likely low-redshift contaminants. Three of our z ∼ 12 candidates push the limits of early stellar mass assembly: they have estimated stellar masses ∼ 5 × 109 M ⊙, implying an effective stellar baryon fraction of ϵ ⋆ ∼ 0.2−0.5, where ϵ ⋆ ≡ M ⋆/(f b M halo). The assembly of such stellar reservoirs is made possible due to rapid, burst-driven star formation on timescales < 100 Myr where the star formation rate may far outpace the growth of the underlying dark matter halos. This is supported by the similar volume densities inferred for M ⋆ ∼ 1010 M ⊙ galaxies relative to M ⋆ ∼ 109 M ⊙—both about 10−6 Mpc−3—implying they live in halos of comparable mass. At such high redshifts, the duty cycle for starbursts would be of order unity, which could cause the observed change in the shape of the UV luminosity function from a double power law to a Schechter function at z ≈ 8. Spectroscopic redshift confirmation and ensuing constraints of their masses will be critical to understand how, and if, such early massive galaxies push the limits of galaxy formation in the Lambda cold dark matter paradigm.

We combine deep imaging data from the CEERS early release JWST survey and Hubble Space Telescope imaging from CANDELS to examine the size–mass relation of star-forming galaxies and the morphology–quenching relation at stellar masses M ⋆ ≥ 109.5 M ⊙ over the redshift range 0.5 < z < 5.5. In this study with a sample of 2450 galaxies, we separate star-forming and quiescent galaxies based on their star formation activity and confirm that star-forming and quiescent galaxies have different morphologies out to z = 5.5, extending the results of earlier studies out to higher redshifts. We find that star-forming and quiescent galaxies have typical Sérsic indices of n ∼ 1.3 and n ∼ 4.3, respectively. Focusing on star-forming galaxies, we find that the slope of the size–mass relation is nearly constant with redshift, as was found previously, but shows a modest increase at z ∼ 4.2. The intercept in the size–mass relation declines out to z = 5.5 at rates that are similar to what earlier studies found. The intrinsic scatter in the size–mass relation is relatively constant out to z = 5.5.

We present the star formation rate–stellar mass (SFR–M*) relation for galaxies in the Cosmic Evolution Early Release Science survey at 4.5 ≤ z ≤ 12. We model the JWST and Hubble Space Telescope rest-UV and rest-optical photometry of galaxies with flexible star formation histories (SFHs) using BAGPIPES. We consider SFRs averaged from the SFHs over 10 Myr (SFR10) and 100 Myr (SFR100), where the photometry probes SFRs on these timescales, effectively tracing nebular emission lines in the rest-optical (on ~10 Myr timescales) and the UV/optical continuum (on ~100 Myr timescales). We measure the slope, normalization and intrinsic scatter of the SFR–M* relation, taking into account the uncertainty and the covariance of galaxy SFRs and M*. From z ~ 5 to 9 there is larger scatter in the SFR10–M* relation, with σ(logSFR100)=0.4 dex, compared to the SFR100–M* relation, with σ(logSFR10)=0.1 dex. This scatter increases with redshift and increasing stellar mass, at least out to z ~ 7. These results can be explained if galaxies at higher redshift experience an increase in star formation variability and form primarily in short, active periods, followed by a lull in star formation (i.e., “napping” phases). We see a significant trend in the ratio RSFR = SFR10/SFR100 in which, on average, RSFR decreases with increasing stellar mass and increasing redshift. This yields a star formation “duty cycle” of ~40% for galaxies with logM*/M⊙≥9.3 at z ~ 5, declining to ~20% at z ~ 9. Galaxies also experience longer lulls in star formation at higher redshift and at higher stellar mass, such that galaxies transition from periods of higher SFR variability at z ≳ 6 to smoother SFR evolution at z ≲ 4.5.

We select and characterize a sample of massive (log(M */M ⊙) > 10.6) quiescent galaxies (QGs) at 3 < z < 5 in the latest Cosmological Evolution Survey catalog (COSMOS2020). QGs are selected using a new rest-frame color-selection method, based on their probability of belonging to the quiescent group defined by a Gaussian mixture model (GMM) trained on rest-frame colors (NUV − U, U − V, V − J) of similarly massive galaxies at 2 < z < 3. We calculate the quiescent probability threshold above which a galaxy is classified as quiescent using simulated galaxies from the shark semi-analytical model. We find that, at z ≥ 3 in shark, the GMM/NUVU − VJ method outperforms classical rest-frame UVJ selection and is a viable alternative. We select galaxies as quiescent based on their probability in COSMOS2020 at 3 < z < 5, and compare the selected sample to both UVJ- and NUVrJ-selected samples. We find that, although the new selection matches UVJ and NUVrJ in number, the overlap between color selections is only ∼50%–80%, implying that rest-frame color commonly used at lower-redshift selections cannot be equivalently used at z > 3. We compute median rest-frame spectral energy distributions for our sample and find the median QG at 3 < z < 5 has a strong Balmer/4000 Å break, and residual NUV flux indicating recent quenching. We find the number densities of the entire quiescent population (including post-starbursts) more than doubles from 3.5 ± 2.2 × 10−6 Mpc−3 at 4 < z < 5 to 1.4 ± 0.4 × 10−5 Mpc−3 at 3 < z < 4, confirming that the onset of massive galaxy quenching occurs as early as 3 < z < 5.

Recent stellar chemical abundance measurements of a handful of z ∼ 2 quiescent galaxies have suggested these galaxies exhibit a remarkably strong α-enhancement compared to their local and intermediate-redshift counterparts. This apparent chemical evolution following quenching suggests that even the innermost regions of massive early-type galaxies may have experienced substantial mixing of stars in mergers, challenging a purely inside-out growth model. However, larger samples are needed to determine whether a high α-enhancement ([Mg/Fe] ≈0.5) is common in z ∼ 2 quiescent galaxies, and a comparative analysis is needed to determine whether it is consistently inferred using different stellar population synthesis models. We report age and stellar chemical abundance measurements for a sample of four gravitationally lensed quiescent galaxies at z ∼ 2.1–2.65 based on Magellan/FIRE spectroscopy. For three of these galaxies we constrain the α-enhancement, and in two cases we measure high values comparable to earlier results when the spectra are analyzed consistently. We also find that the choice of modeling approach can exert a significant effect on the measured abundances. This model dependence can be partly, but not entirely, explained by the complex abundance patterns of α-elements in galaxies, which has been observed at lower redshifts and in one z ∼ 2 quiescent galaxy. Our investigation highlights the importance of independently varying abundance of α-elements when fitting the spectra of such galaxies. Observations with JWST will soon deliver precise and spatially resolved abundances of these and other quiescent galaxies at cosmic noon, opening a new window into their evolution.

We present the first comprehensive release of photometric redshifts (photo- z's) from the Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (CANDELS) team. We use statistics based upon the Quantile–Quantile (Q–Q) plot to identify biases and signatures of underestimated or overestimated errors in photo- z probability density functions (PDFs) produced by six groups in the collaboration; correcting for these effects makes the resulting PDFs better match the statistical definition of a PDF. After correcting each group’s PDF, we explore three methods of combining the different groups’ PDFs for a given object into a consensus curve. Two of these methods are based on identifying the minimum f-divergence curve, i.e., the PDF that is closest in aggregate to the other PDFs in a set (analogous to the median of an array of numbers). We demonstrate that these techniques yield improved results using sets of spectroscopic redshifts independent of those used to optimize PDF modifications. The best photo- z PDFs and point estimates are achieved with the minimum f-divergence using the best four PDFs for each object (mFDa4) and the hierarchical Bayesian (HB4) methods, respectively. The HB4 photo- z point estimates produced σ NMAD = 0.0227/0.0189 and ∣Δz/(1 + z)∣ > 0.15 outlier fraction = 0.067/0.019 for spectroscopic and 3D Hubble Space Telescope redshifts, respectively. Finally, we describe the structure and provide guidance for the use of the CANDELS photo- z catalogs, which are available at https://archive.stsci.edu/prepds/candels/.

We present results from Atacama Large Millimeter/submillimeter Array (ALMA) 1.2 mm continuum observations of a sample of 27 star-forming galaxies at z = 2.1–2.5 from the MOSFIRE Deep Evolution Field survey with metallicity and star formation rate measurements from optical emission lines. Using stacks of Spitzer, Herschel, and ALMA photometry (rest frame ∼8–400 μm), we examine the infrared (IR) spectral energy distributions (SED) of z ∼ 2.3 subsolar-metallicity (∼0.5 Z ⊙) luminous infrared galaxies (LIRGs). We find that the data agree well with an average template of higher-luminosity local low-metallicity dwarf galaxies (reduced χ 2 = 1.8). When compared with the commonly used templates for solar-metallicity local galaxies or high-redshift LIRGs and ultraluminous IR galaxies, even in the most favorable case (with reduced χ 2 = 2.8), the templates are rejected at >98% confidence. The broader and hotter IR SED of both the local dwarfs and high-redshift subsolar-metallicity galaxies may result from different grain properties or a harder/more intense ionizing radiation field that increases the dust temperature. The obscured star formation rate (SFR) indicated by the far-IR emission of the subsolar-metallicity galaxies is only ∼60% of the total SFR, considerably lower than that of the local LIRGs with ∼96%–97% obscured fractions. Due to the evolving IR SED shape, the local LIRG templates fit to mid-IR data overestimate the Rayleigh–Jeans tail measurements by a factor of 2–20. These templates underestimate IR luminosities if fit to the observed ALMA fluxes by >0.4 dex. At a given stellar mass or metallicity, dust masses at z ∼ 2.3 are an order of magnitude higher than z ∼ 0. Given the predicted molecular gas fractions, the observed z ∼ 2.3 dust-to-stellar mass ratios suggest lower dust-to-molecular gas masses than in local galaxies with similar metallicities.

Accurately distinguishing between quiescent and star-forming galaxies is essential for understanding galaxy evolution. Traditional methods, such as spectral energy distribution (SED) fitting, can be computationally expensive and may struggle to capture complex galaxy properties. This study aims to develop a robust and efficient machine learning (ML) classification method to identify quiescent and star-forming galaxies within the Farmer COSMOS2020 catalog. We utilized JWST wide-field light cones from the Santa Cruz semianalytical modeling framework to train a supervised ML model, the CatBoostClassifier, using 28 color features derived from eight mutual photometric bands within the COSMOS catalog. The model was validated against a testing set and compared to the SED-fitting method in terms of precision, recall, F1 score, and execution time. Preprocessing steps included addressing missing data, injecting observational noise, and applying a magnitude cut (mch1 < 26 AB) along with a redshift range of 0.2 < z < 3.5 to align the simulated and observational data sets. The ML method achieved an F1 score of 89% for quiescent galaxies, significantly outperforming the SED-fitting method, which achieved 54%. The ML model demonstrated superior recall (88% versus 38%) while maintaining comparable precision. When applied to the COSMOS2020 catalog, the ML model predicted a systematically higher fraction of quiescent galaxies across all redshift bins within 0.2 < z < 3.5 compared to traditional methods like NUVrJ and SED-fitting. This study shows that ML, combined with multiwavelength data, can effectively identify quiescent and star-forming galaxies, providing valuable insights into galaxy evolution. The trained classifier and full classification catalog are publicly available.

Identifying active galactic nuclei (AGNs) in dwarf galaxies is critical for understanding black hole formation but remains observationally challenging due to their low luminosities, metallicities, and star formation–driven emission that can obscure AGN signatures. Machine learning techniques, particularly unsupervised methods, offer new ways to address these challenges by uncovering patterns in complex, multiwavelength data. In this study, we apply Self-Organizing Maps (SOMs) to explore the spectral energy distribution (SED) manifold of dwarf galaxies and evaluate AGN selection biases across various diagnostics. We train a 51 × 51 SOM on 30,344 dwarf galaxies (z < 0.055, M* < 109.5M⊙) from the NSA catalog using nine-band photometry spanning near-UV to mid-infrared. A set of 438 previously identified dwarf AGNs, selected via mid-infrared color, optical emission lines, X-ray, optical variability, and broad-line features, was mapped onto the SOM. AGNs identified by different methods occupy distinct and partially overlapping regions in SED space, reflecting biases related to host galaxy properties. Wide-field Infrared Survey Explorer (WISE)-selected AGNs are strongly concentrated in lower-mass regions and form two distinct clumps: one associated with bluer, starburst-like systems and the other with redder galaxies showing spectral features more typical of AGNs. This separation may help disentangle true AGN hosts from starburst contaminants in WISE-selected samples. Additionally, AGNs selected via various diagnostics tend to avoid regions of strong star formation, while a subset of lower-mass AGNs occupy SOM regions indicative of high AGN luminosity relative to their stellar content. Our results demonstrate the utility of manifold learning in refining AGN selection in the low-mass regime.

To study the role of environment in galaxy evolution, we reconstruct the underlying density field of galaxies based on COSMOS2020 (The Farmer catalog) and provide the density catalog for a magnitude-limited (K s < 24.5) sample of ∼210,000 galaxies at 0.4 < z < 5 within the COSMOS field. The environmental densities are calculated using a weighted kernel density estimation approach with the choice of a von Mises–Fisher kernel, an analog of the Gaussian kernel for periodic data. Additionally, we make corrections for the edge effect and masked regions in the field. We utilize physical properties extracted by LePhare to investigate the connection between star formation activity and the environmental density of galaxies in six mass-complete subsamples at different cosmic epochs within 0.4 < z < 4. Our findings confirm a strong anticorrelation between star formation rate (SFR)/specific SFR (sSFR) and environmental density out to z ∼ 1.1. At 1.1 < z < 2, there is no significant correlation between SFR/sSFR and density. At 2 < z < 4, we observe a reversal of the SFR/sSFR–density relation such that both SFR and sSFR increase by a factor of ∼10 with increasing density contrast, δ, from −0.4 to 5. This observed reversal at higher redshifts supports the scenario where an increased availability of gas supply, along with tidal interactions and a generally higher star formation efficiency in dense environments, could potentially enhance star formation activity in galaxies located in rich environments at z > 2.