756 research outputs found
Searching for Dark Matter Annihilation in the Smith High-Velocity Cloud
Recent observations suggest that some high-velocity clouds may be confined by
massive dark matter halos. In particular, the proximity and proposed dark
matter content of the Smith Cloud make it a tempting target for the indirect
detection of dark matter annihilation. We argue that the Smith Cloud may be a
better target than some Milky Way dwarf spheroidal satellite galaxies and use
gamma-ray observations from the Fermi Large Area Telescope to search for a dark
matter annihilation signal. No significant gamma-ray excess is found coincident
with the Smith Cloud, and we set strong limits on the dark matter annihilation
cross section assuming a spatially-extended dark matter profile consistent with
dynamical modeling of the Smith Cloud. Notably, these limits exclude the
canonical thermal relic cross section () for dark matter masses GeV annihilating via the or channels for certain assumptions of the dark matter
density profile; however, uncertainties in the dark matter content of the Smith
Cloud may significantly weaken these constraints.Comment: 7 pages, 5 figures. Published in Ap
Milky way satellite census. I. The observational selection function for milky way satellites in DES Y3 and pan-STARRS DR1
Artículo escrito por un elevado número de autores, sólo se referencian el que aparece en primer lugar, los autores pertenecientes a la UAM y el nombre del grupo de colaboración, si lo hubiereWe report the results of a systematic search for ultra-faint Milky Way satellite galaxies using data from the Dark Energy Survey (DES) and Pan-STARRS1 (PS1). Together, DES and PS1 provide multi-band photometry in optical/near-infrared wavelengths over ∼80% of the sky. Our search for satellite galaxies targets ∼25,000 deg2 of the high-Galactic-latitude sky reaching a 10σ point-source depth of ⪆22.5 mag in the g and r bands. While satellite galaxy searches have been performed independently on DES and PS1 before, this is the first time that a self-consistent search is performed across both data sets. We do not detect any new high-significance satellite galaxy candidates, recovering the majority of satellites previously detected in surveys of comparable depth. We characterize the sensitivity of our search using a large set of simulated satellites injected into the survey data. We use these simulations to derive both analytic and machine-learning models that accurately predict the detectability of Milky Way satellites as a function of their distance, size, luminosity, and location on the sky. To demonstrate the utility of this observational selection function, we calculate the luminosity function of Milky Way satellite galaxies, assuming that the known population of satellite galaxies is representative of the underlying distribution. We provide access to our observational selection function to facilitate comparisons with cosmological models of galaxy formation and evolutionThe DES data management system is supported by the National Science Foundation under grant Nos. AST-1138766 and AST1536171. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2015-71825,
ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds
from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013), including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) e-Universe (CNPq grant 465376/2014-2
A machine learning approach to the detection of ghosting and scattered light artifacts in dark energy survey images
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”) and 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 are 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
Identifying RR lyrae variable stars in six years of the dark energy survey
We present a search for RR Lyrae stars using the full six-year data set from the Dark Energy Survey covering ∼5000 deg2 of the southern sky. Using a multistage multivariate classification and light-curve template-fitting scheme, we identify RR Lyrae candidates with a median of 35 observations per candidate. We detect 6971 RR Lyrae candidates out to ∼335 kpc, and we estimate that our sample is >70% complete at ∼150 kpc. We find excellent agreement with other wide-area RR Lyrae catalogs and RR Lyrae studies targeting the Magellanic Clouds and other Milky Way satellite galaxies. We fit the smooth stellar halo density profile using a broken-power-law model with fixed halo flattening (q = 0.7), and we find strong evidence for a break at = - R 32.1+ kpc 0 0.9 1.1 with an inner slope of = - - n 2.54+ 1 0.09 0.09 and an outer slope of = - - n 5.42+ 2 0.14 0.13. We use our catalog to perform a search for Milky Way satellite galaxies with large sizes and low luminosities. Using a set of simulated satellite galaxies, we find that our RR Lyrae-based search is more sensitive than those using resolved stellar populations in the regime of large (rh 500 pc), low-surface-brightness dwarf galaxies. A blind search for large, diffuse satellites yields three candidate substructures. The first can be confidently associated with the dwarf galaxy Eridanus II. The second has a distance and proper motion similar to the ultrafaint dwarf galaxy Tucana II but is separated by ∼5 deg. The third is close in projection to the globular cluster NGC 1851 but is ∼10 kpc more distant and appears to differ in proper motion. © 2021 Institute of Physics Publishing. All rights reserved
Search for gamma-ray emission from dark matter annihilation in the Small Magellanic Cloud with the Fermi Large Area Telescope
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