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Abstract We report the discovery of three faint and ultrafaint dwarf galaxies—Sculptor A, Sculptor B, and Sculptor C—in the direction of NGC 300 (D= 2.0 Mpc), a Large Magellanic Cloud–mass galaxy. Deep ground-based imaging with Gemini/GMOS resolves all three dwarf galaxies into stars, each displaying a red giant branch indicative of an old, metal-poor stellar population. No young stars or Higas are apparent, and the lack of a GALEX UV detection suggests that all three systems are quenched. Sculptor C (D= 2.04 ${}_{-0.13}^{+0.10}$Mpc;M_V=  −9.1 ± 0.1 mag orL_V= (3.7 ${}_{-0.3}^{+0.4}$) × 10⁵L_⊙) is consistent with being a satellite of NGC 300. Sculptor A (D= 1.35 ${}_{-0.08}^{+0.22}$Mpc;M_V= −6.9 ± 0.3 mag orL_V= (5 ${}_{-1}^{+1}$) × 10⁴L_⊙) is likely in the foreground of NGC 300 and at the extreme edge of the Local Group, analogous to the recently discovered ultrafaint Tucana B in terms of its physical properties and environment. Sculptor B (D= 2.48 ${}_{-0.24}^{+0.21}$Mpc;M_V= −8.1 ± 0.3 mag orL_V= (1.5 ${}_{-0.4}^{+0.5}$) × 10⁵L_⊙) is likely in the background, but future distance measurements are necessary to solidify this statement. It is also of interest due to its quiescent state and low stellar mass. Both Sculptor A and B are ≳2–4r_virfrom NGC 300 itself. The discovery of three dwarf galaxies in isolated or low-density environments offers an opportunity to study the varying effects of ram-pressure stripping, reionization, and internal feedback in influencing the star formation history of the faintest stellar systems. 

Measuring the structural parameters (size, total brightness, light concentration, etc.) of galaxies is a significant first step towards a quantitative description of different galaxy populations. In this work, we demonstrate that a Bayesian Neural Network (BNN) can be used for the inference, with uncertainty quantification, of such morphological parameters from simulated low-surface brightness galaxy images. Compared to traditional profile-fitting methods, we show that the uncertainties obtained using BNNs are comparable in magnitude, well-calibrated, and the point estimates of the parameters are closer to the true values. Our method is also significantly faster, which is very important with the advent of the era of large galaxy surveys and big data in astrophysics. 

Abstract Modern surveys of gravitational microlensing events have progressed to detecting thousands per year, and surveys are capable of probing Galactic structure, stellar evolution, lens populations, black hole physics, and the nature of dark matter. One of the key avenues for doing this is the microlensing Einstein radius crossing time ( t E ) distribution. However, systematics in individual light curves as well as oversimplistic modeling can lead to biased results. To address this, we developed a model to simultaneously handle the microlensing parallax due to Earth's motion, systematic instrumental effects, and unlensed stellar variability with a Gaussian process model. We used light curves for nearly 10,000 OGLE-III and -IV Milky Way bulge microlensing events and fit each with our model. We also developed a forward model approach to infer the t E distribution by forward modeling from the data rather than using point estimates from individual events. We find that modeling the variability in the baseline removes a source of significant bias in individual events, and the previous analyses overestimated the number of t E > 100 day events due to their oversimplistic model ignoring parallax effects. We use our fits to identify the hundreds filling a regime in the microlensing parameter space that are 50% pure of black holes. Finally, we have released the largest-ever catalog of Markov Chain Monte Carlo parameter estimates for microlensing events. 

Abstract High-quality Extragalactic Legacy-field Monitoring (HELM) is a long-term observing program that photometrically monitors several well-studied extragalactic legacy fields with the Dark Energy Camera (DECam) imager on the CTIO 4 m Blanco telescope. Since 2019 February, HELM has been monitoring regions within COSMOS, XMM-LSS, CDF-S, S-CVZ, ELAIS-S1, and SDSS Stripe 82 with few-day cadences in the (u)gri(z) bands, over a collective sky area of ∼38 deg². The main science goal of HELM is to provide high-quality optical light curves for a large sample of active galactic nuclei (AGNs), and to build decades-long time baselines when combining past and future optical light curves in these legacy fields. These optical images and light curves will facilitate the measurements of AGN reverberation mapping lags, as well as studies of AGN variability and its dependencies on accretion properties. In addition, the time-resolved and coadded DECam photometry will enable a broad range of science applications from galaxy evolution to time-domain science. We describe the design and implementation of the program and present the first data release that includes source catalogs and the first ∼3.5 yr of light curves during 2019A–2022A. 

Abstract We are conducting a survey using twilight time on the Dark Energy Camera with the Blanco 4 m telescope in Chile to look for objects interior to Earth’s and Venus’ orbits. To date we have discovered two rare Atira/Apohele asteroids, 2021 LJ4 and 2021 PH27, which have orbits completely interior to Earth’s orbit. We also discovered one new Apollo-type Near Earth Object (NEO) that crosses Earth’s orbit, 2022 AP7. Two of the discoveries have diameters ≳1 km. 2022 AP7 is likely the largest Potentially Hazardous Asteroid (PHA) discovered in about eight years. To date we have covered 624 square degrees of sky near to and interior to the orbit of Venus. The average images go to 21.3 mag in the r band, with the best images near 22nd mag. Our new discovery 2021 PH27 has the smallest semimajor axis known for an asteroid, 0.4617 au, and the largest general relativistic effects (53 arcsec/century) known for any body in the solar system. The survey has detected ∼15% of all known Atira NEOs. We put strong constraints on any stable population of Venus co-orbital resonance objects existing, as well as the Atira and Vatira asteroid classes. These interior asteroid populations are important to complete the census of asteroids near Earth, including some of the most likely Earth impactors that cannot easily be discovered in other surveys. Comparing the actual population of asteroids found interior to Earth and Venus with those predicted to exist by extrapolating from the known population exterior to Earth is important to better understand the origin, composition, and structure of the NEO population. 

Low surface brightness galaxies (LSBGs), galaxies that are fainter than the dark night sky, are famously difficult to detect. Nonetheless, studies of these galaxies are essential to improve our understanding of the formation and evolution of low-mass galaxies. In this work, we train a deep learning model using the Mask R-CNN framework on a set of simulated LSBGs inserted into images from the Dark Energy Survey (DES) Data Release 2 (DR2). This deep learning model is combined with several conventional image pre-processing steps to develop a pipeline for the detection of LSBGs. We apply this pipeline to the full DES DR2 coadd image dataset, and preliminary results show the detection of 22 large, high-quality LSBG candidates that went undetected by conventional algorithms. Furthermore, we find that the performance of our algorithm is greatly improved by including examples of false positives as an additional class during training. 

Wide-field astronomical surveys are often affected by the presence of undesirable reflections (often known as “ghosting artifacts” or “ghosts”) and scattered-light artifacts. The identification and mitigation of these artifacts is important for rigorous astronomical analyses of faint and low-surface-brightness systems. In this work, we use images from the Dark Energy Survey (DES) to train, validate, and test a deep neural network (Mask R-CNN) to detect and localize ghosts and scatteredlight artifacts. We find that the ability of the Mask R-CNN model to identify affected regions is superior to that of conventional algorithms that model the physical processes that lead to such artifacts, thus providing a powerful technique for the automated detection of ghosting and scattered-light artifacts in current and near-future surveys. 

Abstract Stellar streams are excellent probes of the underlying gravitational potential in which they evolve. In this work, we fit dynamical models to five streams in the Southern Galactic hemisphere, combining observations from the Southern Stellar Stream Spectroscopic Survey ( S 5 ), Gaia EDR3, and the Dark Energy Survey, to measure the mass of the Large Magellanic Cloud (LMC). With an ensemble of streams, we find a mass of the LMC ranging from ∼14–19 × 10 10 M ⊙ , probed over a range of closest approach times and distances. With the most constraining stream (Orphan–Chenab), we measure an LMC mass of 18.8 − 4.0 + 3.5 × 10 10 M ⊙ , probed at a closest approach time of 310 Myr and a closest approach distance of 25.4 kpc. This mass is compatible with previous measurements, showing that a consistent picture is emerging of the LMC’s influence on structures in the Milky Way. Using this sample of streams, we find that the LMC’s effect depends on the relative orientation of the stream and LMC at their point of closest approach. To better understand this, we present a simple model based on the impulse approximation and we show that the LMC’s effect depends both on the magnitude of the velocity kick imparted to the stream and the direction of this kick.