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Register a new userSpherical Jeans analysis for dark matter indirect detection in dwarf spheroidal galaxies - Impact of physical parameters and triaxiality
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Dwarf spheroidal (dSph) galaxies are among the most promising targets for the indirect detection of dark matter (DM) from annihilation and/or decay products. Empirical estimates of their DM content - and hence the magnitudes of expected signals - rely on inferences from stellar-kinematic data. However, various kinematic analyses can give different results and it is not obvious which are most reliable. Using extensive sets of mock data of various sizes (mimicking ultra-faint and classical dSphs) and an MCMC engine, here we investigate biases, uncertainties, and limitations of analyses based on parametric solutions to the spherical Jeans equation. For a variety of functional forms for the tracer and DM density profiles, as well as the orbital anisotropy profile, we examine reliability of estimates for the astrophysical J- and D-factors for annihilation and decay, respectively. For large (N > 1000) stellar-kinematic samples typical of classical dSphs, errors tend to be dominated by systematics, which can be reduced through the use of sufficiently general and flexible functional forms. For small (N < 100) samples typical of ultrafaints, statistical uncertainties tend to dominate systematic errors and flexible models are less necessary. We define an optimal strategy that would mitigate sensitivity to priors and other aspects of analyses based on the spherical Jeans equation. We also find that the assumption of spherical symmetry can bias estimates of J (with the 95% credibility intervals not encompassing the true J-factor) when the object is mildly triaxial (axis ratios b/a = 0.8, c/a = 0.6). A concluding table summarises the typical error budget and biases for the different sample sizes considered.
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Line-of-sight integrals of the squared density, commonly called the J-factor, are essential for inferring dark matter annihilation signals. The J-factors of dark matter-dominated dwarf spheroidal satellite galaxies (dSphs) have typically been derived using Bayesian techniques, which for small data samples implies that a choice of priors constitutes a non-negligible systematic uncertainty. Here we report the development of a new fully frequentist approach to construct the profile likelihood of the J-factor. Using stellar kinematic data from several classical and ultra-faint dSphs, we derive the maximum likelihood value for the J-factor and its confidence intervals. We validate this method, in particular its bias and coverage, using simulated data from the Gaia Challenge. We find that the method possesses good statistical properties. The J-factors and their uncertainties are generally in good agreement with the Bayesian-derived values, with the largest deviations restricted to the systems with the smallest kinematic datasets. We discuss improvements, extensions, and future applications of this technique.
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Fermi-LAT observations have strongly constrained dark matter annihilation through the joint-likelihood analysis of dwarf spheroidal galaxies (dSphs). These constraints are expected to be robust because dSphs have measurable dark matter content and produce negligible astrophysical emission. However, each dSph is dim, with a predicted flux that typically falls below the accuracy of the background model. We show that this significantly diminishes the reliability of previous joint-likelihood algorithms, and develop an improved analysis that directly accounts for the effect of background mismodeling. This method produces more robust limits and detections of dark matter in both real and mock data. We calculate improved limits on the dark matter annihilation cross-section, which differ by nearly a factor of two from previous analyses - despite examining identical data.
The distribution of dark matter in dwarf galaxies can have important implications on our understanding of galaxy formation as well as the particle physics properties of dark matter. However, accurately characterizing the dark matter content of dwarf galaxies is challenging due to limited data and complex dynamics that are difficult to accurately model. In this paper, we apply spherical Jeans modeling to simulated stellar kinematic data of spherical, isotropic dwarf galaxies with the goal of identifying the future observational directions that can improve the accuracy of the inferred dark matter distributions in the Milky Way dwarf galaxies. We explore how the dark matter inference is affected by the location and number of observed stars as well as the line-of-sight velocity measurement errors. We use mock observation to demonstrate the difficulty in constraining the inner core/cusp of the dark matter distribution with datasets of fewer than 10,000 stars. We also demonstrate the need for additional measurements to make robust estimates of the expected dark matter annihilation signal strength. For the purpose of deriving robust indirect detection constraints, we identify Ursa Major II, Ursa Minor, and Draco as the systems that would most benefit from additional stars being observed.
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