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Buckley, Matthew R.

Publications and source records attributed to Buckley, Matthew R..

Limits on dark matter annihilation from the shape of radio emission in M31

Well-motivated models of dark matter often result in a population of electrons and positrons within galaxies produced through dark matter annihilation — usually in association with gamma rays. As they diffuse through galactic magnetic fields, these e ± produce synchrotron radio emission. The intensity and morphology of this signal depends on the properties of the interstellar medium through which the e ± propagate. Using observations of the Andromeda Galaxy (M31) to construct a model of the gas, magnetic fields, and starlight, we set constraints on dark matter annihilation to b$\overline{b}$ using the morphology of 3.6 cm radio emission. As the emission signal at the center of M31 is very sensitive to the diffusion coefficient and dark matter profile, we base our limits on the differential flux in the region between 0.9 – 6.9 kpc from the center. We exclude annihilation cross sections ≳ 3 × 10 −25 cm 3 /s in the mass range 10 – 500 GeV, with a maximum sensitivity of 7 × 10 −26 cm 3 /s at 20 – 40 GeV. Though these limits are weaker than those found in previous studies of M31, they are robust to variations of the diffusion coefficient.

79 ASTRONOMY AND ASTROPHYSICS↗

via machinae : Searching for stellar streams using unsupervised machine learning

ABSTRACT We develop a new machine learning algorithm, via machinae, to identify cold stellar streams in data from the Gaia telescope. via machinae is based on ANODE, a general method that uses conditional density estimation and sideband interpolation to detect local overdensities in the data in a model agnostic way. By applying ANODE to the positions, proper motions, and photometry of stars observed by Gaia, via machinae obtains a collection of those stars deemed most likely to belong to a stellar stream. We further apply an automated line-finding method based on the Hough transform to search for line-like features in patches of the sky. In this paper, we describe the via machinae algorithm in detail and demonstrate our approach on the prominent stream GD-1. Though some parts of the algorithm are tuned to increase sensitivity to cold streams, the via machinae technique itself does not rely on astrophysical assumptions, such as the potential of the Milky Way or stellar isochrones. This flexibility suggests that it may have further applications in identifying other anomalous structures within the Gaia data set, for example debris flow and globular clusters.

79 ASTRONOMY AND ASTROPHYSICS↗