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Friday, February 22, 2019 at 1:00pm
Physical Sciences Building, 401
245 East Avenue
Seeing the dark matter halo through Gaia’s eyes with machine learning
Abstract: Understanding the properties of our dark matter halo is relevant to both astrophysics as it informs the formation history of our galaxy, and particle physics in that it impacts the interpretation of dark matter experiments. I will review the assumptions made underlying typical halo models and discuss how such assumptions can lead us astray, making clear data-driven halo modeling would be highly desirable. Recent work has shown low metallicity stars can act as tracers for a substantial fraction of local dark matter, allowing data from the Gaia satellite to potentially be interpreted as a measurement of the halo. However, despite Gaia having observed well over a billon stars, metallicity measurements require cross-correlation with other much smaller (~200,000 star) surveys. With the aid of modern machine learning methods, we seek to find if the tracer stars can be identified only by the kinematic and spectral information available to Gaia. I will present positive results using the FIRE galactic simulations that indicate that it should be possible to “learn the dark matter halo” with much finer resolution than currently known.