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A crucial yet challenging task in galaxy evolution studies is the identification of distant merging galaxies, a task that suffers from a variety of issues ranging from telescope sensitivities and limitations to the inherently chaotic morphologies of young galaxies. In this paper, we use random forests and convolutional neural networks to identify high-redshift JWST Cosmic Evolution Early Release Science Survey (CEERS) galaxy mergers. We train these algorithms on simulated 3 < z < 5 CEERS galaxies created from the IllustrisTNG subhalo morphologies and the Santa Cruz SAM light cone. We apply our models to observed CEERS galaxies at 3 < z < 5. We find that our models correctly classify ∼60%–70% of simulated merging and nonmerging galaxies; better performance on the merger class comes at the expense of misclassifying more nonmergers. We could achieve more accurate classifications, as well as test for a dependency on physical parameters such as gas fraction, mass ratio, and relative orbits, by curating larger training sets. When applied to real CEERS galaxies using visual classifications as ground truth, the random forests correctly classified 40%–60% of mergers and nonmergers at 3 < z < 4 but tended to classify most objects as nonmergers at 4 < z < 5 (misclassifying ∼70% of visually classified mergers). On the other hand, the CNNs tended to classify most objects as mergers across all redshifts (misclassifying 80%–90% of visually classified nonmergers). We investigate what features the models find most useful, as well as the characteristics of false positives and false negatives, and also calculate merger rates derived from the identifications made by the models.

We present the Next Generation Deep Extragalactic Exploratory Public (NGDEEP) Survey, a deep slitless spectroscopic and imaging Cycle 1 JWST treasury survey designed to constrain feedback mechanisms in low-mass galaxies across cosmic time. NGDEEP targets the Hubble Ultra Deep Field (HUDF) with NIRISS slitless spectroscopy ( ${f}_{\mathrm{lim},\mathrm{line},5\sigma }\approx 1.2\,\times \,$ 10 ^−18 erg s ^−1 cm ^−2 ) to measure metallicities and star formation rates (SFRs) for low-mass galaxies through the peak of the cosmic SFR density (0.5 < z < 4). In parallel, NGDEEP targets the HUDF-Par2 parallel field with NIRCam ( ${m}_{\mathrm{lim},5\sigma }=30.6-30.9$ ) to discover galaxies to z > 12, constraining the slope of the faint end of the rest-ultraviolet luminosity function. NGDEEP overlaps with the deepest HST Advanced Camera for Surveys optical imaging in the sky, F435W in the HUDF ( ${m}_{\mathrm{lim},{\rm{F}}435{\rm{W}}}=29.6$ ) and F814W in HUDF-Par2 ( ${m}_{\mathrm{lim},{\rm{F}}814{\rm{W}}}=30$ ), making this a premier HST+JWST deep field. As a treasury survey, NGDEEP data are public immediately, and we will rapidly release data products and catalogs in the spirit of previous deep-field initiatives. In this paper we present the NGDEEP survey design, summarize the science goals, and detail plans for the public release of NGDEEP reduced data products.

We use the Ultraviolet Imaging of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey fields (UVCANDELS) to measure half-light radii in the rest-frame far-UV for ∼16,000 disk-like galaxies over 0.5 ≤ z ≤ 3. We compare these results to rest-frame optical sizes that we measure in a self-consistent way and find that the stellar mass–size relation of disk galaxies is steeper in the rest-frame UV than in the optical across our entire redshift range. We show that this is mainly driven by massive galaxies (≳10 ^10 M _⊙ ), which we find to also be among the most dusty. Our results are consistent with the literature and have commonly been interpreted as evidence of inside-out growth wherein galaxies form their central structures first. However, they could also suggest that the centers of massive galaxies are more heavily attenuated than their outskirts. We distinguish between these scenarios by modeling and selecting galaxies at z = 2 from the VELA simulation suite in a way that is consistent with UVCANDELS. We show that the effects of dust alone can account for the size differences we measure at z = 2. This indicates that, at different wavelengths, size differences and the different slopes of the stellar mass–size relation do not constitute evidence for inside-out growth.

The Next Generation Deep Extragalactic Exploratory Public (NGDEEP) survey program was designed specifically to include Near Infrared Slitless Spectroscopic observations (NGDEEP-NISS) to detect multiple emission lines in as many galaxies as possible and across a wide redshift range using the Near Infrared Imager and Slitless Spectrograph. We present early results obtained from the first set of observations (Epoch 1, 50% of the allocated orbits) of this program (NGDEEP-NISS1). Using a set of independently developed calibration files designed to deal with a complex combination of overlapping spectra, multiple position angles, and multiple cross filters and grisms, in conjunction with a robust and proven algorithm for quantifying contamination from overlapping dispersed spectra, NGDEEP-NISS1 has achieved a 3 σ sensitivity limit of 2 × 10 ^−18 erg s ^−1 cm ^−2 . We demonstrate the power of deep wide field slitless spectroscopy (WFSS) to characterize the star formation rates, and metallicity ([O iii ]/H β ), and dust content, of galaxies at 1 < z < 3.5. The latter showing intriguing initial results on the applicability and assumptions made regarding the use of Case B recombination. Further, we identify the presence of active galactic nuclei and infer the mass of their supermassive black holes using broadened restframe Mg ii and H β emission lines. The spectroscopic results are then compared with the physical properties of galaxies extrapolated from fitting spectral energy distribution models to photometry alone. The results clearly demonstrate the unique power and efficiency of WFSS at near-infrared wavelengths over other methods to determine the properties of galaxies across a broad range of redshifts.