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 Sp
ectroscopic Survey (${S}^5$), Gaia EDR3, and the Dark Energy Survey (DES), 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 to $19 times 10^{10} mathrm{M}_{odot}$, probed over a range of closest approach times and distances. With the most constraining stream (Orphan-Chenab), we measure an LMC mass of $18.8^{+ 3.5}_{- 4.0} times 10^{10} mathrm{M}_{odot}$, 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 LMCs influence on structures in the Milky Way. Using this sample of streams, we find that the LMCs 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 LMCs effect depends both on the magnitude of the velocity kick imparted to the stream and the direction of this kick.
Astronomical images are often plagued by unwanted artifacts that arise from a number of sources including imperfect optics, faulty image sensors, cosmic ray hits, and even airplanes and artificial satellites. Spurious reflections (known as ghosts) an
d the scattering of light off the surfaces of a camera and/or telescope are particularly difficult to avoid. Detecting ghosts and scattered light efficiently in large cosmological surveys that will acquire petabytes of data can be a daunting task. In this paper, we use data from the Dark Energy Survey to develop, train, and validate a machine learning model to detect ghosts and scattered light using convolutional neural networks. The model architecture and training procedure is discussed in detail, and the performance on the training and validation set is presented. Testing is performed on data and results are compared with those from a ray-tracing algorithm. As a proof of principle, we have shown that our method is promising for the Rubin Observatory and beyond.
We perform a detailed photometric and astrometric analysis of stars in the Jet stream using data from the first data release of the DECam Local Volume Exploration Survey (DELVE) DR1 and emph{Gaia} EDR3. We discover that the stream extends over $sim 2
9 ^{circ}$ on the sky (increasing the known length by $18^{circ}$), which is comparable to the kinematically cold Phoenix, ATLAS, and GD-1 streams. Using blue horizontal branch stars, we resolve a distance gradient along the Jet stream of 0.2 kpc/deg, with distances ranging from $D_odot sim 27-34$ kpc. We use natural splines to simultaneously fit the stream track, width, and intensity to quantitatively characterize density variations in the Jet stream, including a large gap, and identify substructure off the main track of the stream. Furthermore, we report the first measurement of the proper motion of the Jet stream and find that it is well-aligned with the stream track suggesting the stream has likely not been significantly perturbed perpendicular to the line of sight. Finally, we fit the stream with a dynamical model and find that the stream is on a retrograde orbit, and is well fit by a gravitational potential including the Milky Way and Large Magellanic Cloud. These results indicate the Jet stream is an excellent candidate for future studies with deeper photometry, astrometry, and spectroscopy to study the potential of the Milky Way and probe perturbations from baryonic and dark matter substructure.
We characterize the response of a novel 250 $mu$m thick, fully-depleted Skipper Charged-Coupled Device (CCD) to visible/near-infrared light with a focus on potential applications for astronomical observations. We achieve stable, single-electron resol
ution with readout noise $sigma sim 0.18$ e$^{-}$ rms/pix from 400 non-destructive measurements of the charge in each pixel. We verify that the gain derived from photon transfer curve measurements agrees with the gain calculated from the quantized charge of individual electrons to within < 1%. We also perform relative quantum efficiency measurements and demonstrate high relative quantum efficiency at optical/near-infrared wavelengths, as is expected for a thick, fully depleted detector. Finally, we demonstrate the ability to perform multiple non-destructive measurements and achieve sub-electron readout noise over configurable subregions of the detector. This work is the first step toward demonstrating the utility of Skipper CCDs for future astronomical and cosmological applications.
We present deep Hubble Space Telescope (HST) photometry of the ultra-faint dwarf galaxy Eridanus II (Eri II). Eri II, which has an absolute magnitude of M_V = -7.1, is located at a distance of 339 kpc, just beyond the virial radius of the Milky Way.
We determine the star formation history of Eri II and measure the structure of the galaxy and its star cluster. We find that a star formation history consisting of two bursts, constrained to match the spectroscopic metallicity distribution of the galaxy, accurately describes the Eri II stellar population. The best-fit model implies a rapid truncation of star formation at early times, with >80% of the stellar mass in place before z~6. A small fraction of the stars could be as young as 8 Gyr, but this population is not statistically significant; Monte Carlo simulations recover a component younger than 9 Gyr only 15% of the time, where they represent an average of 7 +/- 4% of the population. These results are consistent with theoretical expectations for quenching by reionization. The HST depth and angular resolution enable us to show that Eri IIs cluster is offset from the center of the galaxy by a projected distance of 23 +/- 3 pc. This offset could be an indication of a small (~50-75 pc) dark matter core in Eri II. Moreover, we demonstrate that the cluster has a high ellipticity of 0.31 +0.05/-0.06 and is aligned with the orientation of Eri II within 3 +/- 6 degrees, likely due to tides. The stellar population of the cluster is indistinguishable from that of Eri II itself.
Searches for low-surface-brightness galaxies (LSBGs) in galaxy surveys are plagued by the presence of a large number of artifacts (e.g., objects blended in the diffuse light from stars and galaxies, Galactic cirrus, star-forming regions in the arms o
f spiral galaxies, etc.) that have to be rejected through time consuming visual inspection. In future surveys, which are expected to collect hundreds of petabytes of data and detect billions of objects, such an approach will not be feasible. We investigate the use of convolutional neural networks (CNNs) for the problem of separating LSBGs from artifacts in survey images. We take advantage of the fact that, for the first time, we have available a large number of labeled LSBGs and artifacts from the Dark Energy Survey, that we use to train, validate, and test a CNN model. That model, which we call DeepShadows, achieves a test accuracy of $92.0 %$, a significant improvement relative to feature-based machine learning models. We also study the ability to use transfer learning to adapt this model to classify objects from the deeper Hyper-Suprime-Cam survey, and we show that after the model is retrained on a very small sample from the new survey, it can reach an accuracy of $87.6%$. These results demonstrate that CNNs offer a very promising path in the quest to study the low-surface-brightness universe.
The 2020s are poised to continue the past two decades of significant advances based on observations of dwarf galaxies in the nearby universe. Upcoming wide-field photometric surveys will probe substantially deeper than previous data sets, pushing the
discovery frontier for new dwarf galaxies to fainter magnitudes, lower surface brightnesses, and larger distances. These dwarfs will be compelling targets for testing models of galaxy formation and cosmology, including the properties of dark matter and possible modifications to gravity. However, most of the science that can be extracted from nearby dwarf galaxies relies on spectroscopy with large telescopes. We suggest that maximizing the scientific impact of near-future imaging surveys will require both major spectroscopic surveys on 6-10m telescopes and multiplexed spectroscopy with even larger apertures.
Astrophysical observations currently provide the only robust, empirical measurements of dark matter. In the coming decade, astrophysical observations will guide other experimental efforts, while simultaneously probing unique regions of dark matter pa
rameter space. This white paper summarizes astrophysical observations that can constrain the fundamental physics of dark matter in the era of LSST. We describe how astrophysical observations will inform our understanding of the fundamental properties of dark matter, such as particle mass, self-interaction strength, non-gravitational interactions with the Standard Model, and compact object abundances. Additionally, we highlight theoretical work and experimental/observational facilities that will complement LSST to strengthen our understanding of the fundamental characteristics of dark matter.
Since first noticed by Shapley in 1939, a faint object coincident with the Fornax dwarf spheroidal has long been discussed as a possible sixth globular cluster system. However, debate has continued over whether this overdensity is a statistical artif
act or a blended galaxy group. In this Letter we demonstrate, using deep DECam imaging data, that this object is well resolved into stars and is a bona fide star cluster. The stellar overdensity of this cluster is statistically significant at the level of ~ 6 - 6.7 sigma in several different photometric catalogs including Gaia. Therefore, it is highly unlikely to be caused by random fluctuation. We show that Fornax 6 is a star cluster with a peculiarly low surface brightness and irregular shape, which may indicate a strong tidal influence from its host galaxy. The Hess diagram of Fornax 6 is largely consistent with that of Fornax field stars, but it appears to be slightly bluer. However, it is still likely more metal-rich than most of the globular clusters in the system. Faint clusters like Fornax 6 that orbit and potentially get disrupted in the centers of dwarf galaxies can prove crucial for constraining the dark matter distribution in Milky Way satellites.
Astrophysical and cosmological observations currently provide the only robust, empirical measurements of dark matter. Future observations with Large Synoptic Survey Telescope (LSST) will provide necessary guidance for the experimental dark matter pro
gram. This white paper represents a community effort to summarize the science case for studying the fundamental physics of dark matter with LSST. We discuss how LSST will inform our understanding of the fundamental properties of dark matter, such as particle mass, self-interaction strength, non-gravitational couplings to the Standard Model, and compact object abundances. Additionally, we discuss the ways that LSST will complement other experiments to strengthen our understanding of the fundamental characteristics of dark matter. More information on the LSST dark matter effort can be found at https://lsstdarkmatter.github.io/ .
