We report the discovery of a Multi Unit Spectroscopic Explorer (MUSE) galaxy group at z = 4.32 lensed by the massive galaxy cluster ACT-CL J0102-4915 (aka El Gordo) at z = 0.87, associated with a 1.2 mm source that is at a 2.07 ± 0.88 kpc projected distance from one of the group galaxies. Three images of the whole system appear in the image plane. The 1.2 mm source has been detected within the Atacama Large Millimetre/submillimetre Array (ALMA) Lensing Cluster Survey (ALCS). As this ALMA source is undetected at wavelengths λ < 2 μm, its redshift cannot be independently determined, however, the three lensing components indicate that it belongs to the same galaxy group at z = 4.32. The four members of the MUSE galaxy group have low to intermediate stellar masses (∼10 7 -10 10 M e ) and star formation rates (SFRs) of 0.4-24 M e yr −1 , resulting in high specific SFRs (sSFRs) for two of them, which suggest that these galaxies are growing fast (with stellar mass doubling times of only ∼2 × 10 7 yr). This high incidence of starburst galaxies is likely a consequence of interactions within the galaxy group, which is compact and has high velocity dispersion. Based on the magnification-corrected sub-/ millimeter continuum flux density and estimated stellar mass, we infer that the ALMA source is classified as an ordinary ultra-luminous infrared galaxy (with associated dust-obscured SFR ∼ 200-300 M e yr −1 ) and lies on the star formation main sequence. This reported case of an ALMA/MUSE group association suggests that some presumably isolated ALMA sources are in fact signposts of richer star-forming environments at high redshifts.

The Wide Field Camera 3 (WFC3) on the Hubble Space Telescope (HST) enabled the search for the first galaxies observed at z∼8-11 (500-700 Myr after the Big Bang). To continue quantifying the number density of the most luminous galaxies (M AB ∼ −22.0) at the earliest epoch observable with HST, we search for z∼10 galaxies (F125W-dropouts) in archival data from the Brightest of Reionizing Galaxies (BoRG[z8]) survey, originally designed for detection of z∼8 galaxies (F098M-dropouts). By focusing on the deepest 293 arcmin 2 of the data along 62 independent lines of sight, we identify six z∼10 candidates satisfying the color selection criteria, detected at S/N>8 in F160W with M AB =−22.8 to −21.1 if at z=10. Three of the six sources, including the two brightest, are in a single WFC3 pointing (∼4 arcmin 2 ), suggestive of significant clustering, which is expected from bright galaxies at z∼10. However, the two brightest galaxies are too extended to be likely at z∼10, and one additional source is unresolved and possibly a brown dwarf. The remaining three candidates have m AB ∼26, and given the area and completeness of our search, our best estimate is a number density of sources that is marginally higher but consistent at 2σ with searches in legacy fields. Our study highlights that z∼10 searches can yield a small number of candidates, making tailored follow-ups of HST pure-parallel observations viable and effective.

The DECam Local Volume Exploration survey (DELVE) is a 126-night survey program on the 4 m Blanco Telescope at the Cerro Tololo Inter-American Observatory in Chile. DELVE seeks to understand the characteristics of faint satellite galaxies and other resolved stellar substructures over a range of environments in the Local Volume. DELVE will combine new DECam observations with archival DECam data to cover ∼15,000 deg2 of high Galactic latitude (∣b∣ > 10°) southern sky to a 5σ depth of g, r, i, z ∼ 23.5 mag. In addition, DELVE will cover a region of ∼2200 deg2 around the Magellanic Clouds to a depth of g, r, i ∼ 24.5 mag and an area of ∼135 deg2 around four Magellanic analogs to a depth of g, i ∼ 25.5 mag. Here, we present an overview of the DELVE program and progress to date. We also summarize the first DELVE public data release (DELVE DR1), which provides point-source and automatic aperture photometry for ∼520 million astronomical sources covering ∼5000 deg2 of the southern sky to a 5σ point-source depth of g = 24.3 mag, r = 23.9 mag, i = 23.3 mag, and z = 22.8 mag. DELVE DR1 is publicly available via the NOIRLab Astro Data Lab science platform.

We train a deep residual convolutional neural network (CNN) to predict the gasphase metallicity (Z) of galaxies derived from spectroscopic information (Z ≡ 12 + log(O/H)) using only three-band gri images from the Sloan Digital Sky Survey. When trained and tested on 128 × 128-pixel images, the root mean squared error (RMSE) of Z pred − Z true is only 0.085 dex, vastly outperforming a trained random forest algorithm on the same data set (RMSE = 0.130 dex). The amount of scatter in Z pred − Z true decreases with increasing image resolution in an intuitive manner. We are able to use CNN-predicted Z pred and independently measured stellar masses to recover a massmetallicity relation with 0.10 dex scatter. Because our predicted MZR shows no more scatter than the empirical MZR, the difference between Z pred and Z true can not be due to purely random error. This suggests that the CNN has learned a representation of the gas-phase metallicity, from the optical imaging, beyond what is accessible with oxygen spectral lines.

We present “Extending the Satellites Around Galactic Analogs Survey” (xSAGA), a method for identifying low-z galaxies on the basis of optical imaging and results on the spatial distributions of xSAGA satellites around host galaxies. Using spectroscopic redshift catalogs from the SAGA Survey as a training data set, we have optimized a convolutional neural network (CNN) to identify z < 0.03 galaxies from more-distant objects using image cutouts from the DESI Legacy Imaging Surveys. From the sample of >100,000 CNN-selected low-z galaxies, we identify >20,000 probable satellites located between 36–300 projected kpc from NASA-Sloan Atlas central galaxies in the stellar-mass range 9.5 < log ( M ⋆ / M ⊙ ) < 11 . We characterize the incompleteness and contamination for CNN-selected samples and apply corrections in order to estimate the true number of satellites as a function of projected radial distance from their hosts. Satellite richness depends strongly on host stellar mass, such that more-massive host galaxies have more satellites, and on host morphology, such that elliptical hosts have more satellites than disky hosts with comparable stellar masses. We also find a strong inverse correlation between satellite richness and the magnitude gap between a host and its brightest satellite. The normalized satellite radial distribution between 36–300 kpc does not depend on host stellar mass, morphology, or magnitude gap. The satellite abundances and radial distributions we measure are in reasonable agreement with predictions from hydrodynamic simulations. Our results deliver unprecedented statistical power for studying satellite galaxy populations and highlight the promise of using machine-learning for extending galaxy samples of wide-area surveys.