20 research outputs found

    Constraints on the pMSSM from LAT Observations of Dwarf Spheroidal Galaxies

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    We examine the ability for the Large Area Telescope (LAT) to constrain Minimal Supersymmetric Standard Model (MSSM) dark matter through a combined analysis of Milky Way dwarf spheroidal galaxies. We examine the Lightest Supersymmetric Particles (LSPs) for a set of ~71k experimentally valid supersymmetric models derived from the phenomenological-MSSM (pMSSM). We find that none of these models can be excluded at 95% confidence by the current analysis; nevertheless, many lie within the predicted reach of future LAT analyses. With two years of data, we find that the LAT is currently most sensitive to light LSPs (m_LSP < 50 GeV) annihilating into tau-pairs and heavier LSPs annihilating into b-bbar. Additionally, we find that future LAT analyses will be able to probe some LSPs that form a sub-dominant component of dark matter. We directly compare the LAT results to direct detection experiments and show the complementarity of these search methods.Comment: 24 pages, 9 figures, submitted to JCA

    Bailing Out the Milky Way: Variation in the Properties of Massive Dwarfs Among Galaxy-Sized Systems

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    Recent kinematical constraints on the internal densities of the Milky Way's dwarf satellites have revealed a discrepancy with the subhalo populations of simulated Galaxy-scale halos in the standard CDM model of hierarchical structure formation. This has been dubbed the "too big to fail" problem, with reference to the improbability of large and invisible companions existing in the Galactic environment. In this paper, we argue that both the Milky Way observations and simulated subhalos are consistent with the predictions of the standard model for structure formation. Specifically, we show that there is significant variation in the properties of subhalos among distinct host halos of fixed mass and suggest that this can reasonably account for the deficit of dense satellites in the Milky Way. We exploit well-tested analytic techniques to predict the properties in a large sample of distinct host halos with a variety of masses spanning the range expected of the Galactic halo. The analytic model produces subhalo populations consistent with both Via Lactea II and Aquarius, and our results suggest that natural variation in subhalo properties suffices to explain the discrepancy between Milky Way satellite kinematics and these numerical simulations. At least ~10% of Milky Way-sized halos host subhalo populations for which there is no "too big to fail" problem, even when the host halo mass is as large as M_host = 10^12.2 h^-1 M_sun. Follow-up studies consisting of high-resolution simulations of a large number of Milky Way-sized hosts are necessary to confirm our predictions. In the absence of such efforts, the "too big to fail" problem does not appear to be a significant challenge to the standard model of hierarchical formation. [abridged]Comment: 12 pages, 3 figures; accepted by JCAP. Replaced with published versio

    The Large Magellanic Cloud in the SDSS and LCDM: Is There A “Found Satellites Problem”?

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    Substructure in ΛCDM provides a number of interesting puzzles. While the missing satellites problem is well-studied, there are suggestions of an opposite problem on the bright end. Subhalos large enough to host luminous satellites are uncommon, so the existence of the Large Magellanic Cloud (LMC) orbiting the Galaxy can potentially be a challenge for ΛCDM. Hence, we describe a search for analogs to an isolated galaxy pair like the Milky Way/LMC system in the SDSS and interpret these results with cosmological simulations. We note that while the LMC may not be unusual based on its luminosity, it is remarkably blue for such satellites. Thus, color may have implications for the LMC’s orbital history

    Extending the SAGA Survey (xSAGA). I. Satellite Radial Profiles as a Function of Host-galaxy Properties

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    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 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. © 2022. The Author(s). Published by the American Astronomical Society.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]
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