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The mass distribution in massive elliptical galaxies encodes their evolutionary history, thus providing an avenue to constrain the baryonic astrophysics in their evolution. The power-law assumption for the radial mass profile in ellipticals has been sufficient to describe several observables to the noise level, including strong lensing and stellar dynamics. In this paper, we quantitatively constrained any deviation, or the lack thereof, from the power-law mass profile in massive ellipticals through joint lensing–dynamics analysis of a large statistical sample with 77 galaxy–galaxy lens systems. We performed an improved and uniform lens modelling of these systems from archival Hubble Space Telescope imaging using the automated lens modelling pipeline dolphin. We combined the lens model posteriors with the stellar dynamics to constrain the deviation from the power law after accounting for the line-of-sight lensing effects, a first for analyses on galaxy–galaxy lenses. We find that the Sloan Lens ACS Survey lens galaxies with a mean redshift of 0.2 are consistent with the power-law profile within 1.1σ (2.8σ) and the Strong Lensing Legacy Survey lens galaxies with a mean redshift of 0.6 are consistent within 0.8σ (2.1σ), for a spatially constant (Osipkov–Merritt) stellar anisotropy profile. We adopted the spatially constant anisotropy profile as our baseline choice based on previous dynamical observables of local ellipticals. However, spatially resolved stellar kinematics of lens galaxies are necessary to differentiate between the two anisotropy models. Future studies will use our lens models to constrain the mass distribution individually in the dark matter and baryonic components.

 

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 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. 

Abstract We report the kinematic, orbital, and chemical properties of 12 stellar streams with no evident progenitors using line-of-sight velocities and metallicities from the Southern Stellar Stream Spectroscopic Survey ( S 5 ), proper motions from Gaia EDR3, and distances derived from distance tracers or the literature. This data set provides the largest homogeneously analyzed set of streams with full 6D kinematics and metallicities. All streams have heliocentric distances between ∼10 and 50 kpc. The velocity and metallicity dispersions show that half of the stream progenitors were disrupted dwarf galaxies (DGs), while the other half originated from disrupted globular clusters (GCs), hereafter referred to as DG and GC streams. Based on the mean metallicities of the streams and the mass–metallicity relation, the luminosities of the progenitors of the DG streams range between those of Carina and Ursa Major I (−9.5 ≲ M V ≲ −5.5). Four of the six GC streams have mean metallicities of [Fe/H] < −2, more metal poor than typical Milky Way (MW) GCs at similar distances. Interestingly, the 300S and Jet GC streams are the only streams on retrograde orbits in our dozen-stream sample. Finally, we compare the orbital properties of the streams with known DGs and GCs in the MW, finding several possible associations. Some streams appear to have been accreted with the recently discovered Gaia–Enceladus–Sausage system, and others suggest that GCs were formed in and accreted together with the progenitors of DG streams whose stellar masses are similar to those of Draco to Carina (∼10 5 –10 6 M ⊙ ). 

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. 

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. 

We present photometric and spectroscopic observations of the extraordinary gamma-ray burst (GRB) 221009A in search of an associated supernova. Some past GRBs have shown bumps in the optical light curve that coincide with the emergence of supernova spectral features, but we do not detect any significant light-curve features in GRB 221009A, nor do we detect any clear sign of supernova spectral features. Using two well-studied GRB-associated supernovae (SN 2013dx,$M_{r,\max }=-19.54;$SN 2016jca,$M_{r,\max }=-19.04$) at a similar redshift as GRB 221009A (z= 0.151), we modeled how the emergence of a supernova would affect the light curve. If we assume the GRB afterglow to decay at the same rate as the X-ray data, the combination of afterglow and a supernova component is fainter than the observed GRB brightness. For the case where we assume the best-fit power law to the optical data as the GRB afterglow component, a supernova contribution should have created a clear bump in the light curve, assuming only extinction from the Milky Way. If we assume a higher extinction ofE(B−V) = 1.74 mag (as has been suggested elsewhere), the supernova contribution would have been hard to detect, with a limit on the associated supernova of$M_{r,\max }\approx -$19.54. We do not observe any clear supernova features in our spectra, which were taken around the time of expected maximum light. The lack of a bright supernova associated with GRB 221009A may indicate that the energy from the explosion is mostly concentrated in the jet, leaving a lower energy budget available for the supernova.