25–28 Mar 2020
UCLA
US/Pacific timezone

Uncertainties in Direct Dark Matter Detection in Light of Gaia's Escape Velocity Measurements

26 Mar 2020, 18:15
15m
PAB- 1-425 (UCLA)

PAB- 1-425

UCLA

UCLA Department of Physics and Astronomy 475 Portola Plaza, Los Angeles, CA 90095
Talk Dark matter theory Session 10

Speaker

Youjia Wu (University of Michigan)

Description

Direct detection experiments have set increasingly stringent limits on the cross section for spin-independent dark matter-nucleon interactions. In obtaining such limits, experiments primarily assume the standard halo model (SHM) as the distribution of dark matter in our Milky Way. Three astrophysical parameters are required to define the SHM: the local dark matter escape velocity, the local dark matter density and the circular velocity of the sun around the center of the galaxy. This work studies the effect of the uncertainties in these three astrophysical parameters on the XENON1T exclusion limits using the publicly available DDCalc code. We compare limits obtained using the widely assumed escape velocity from the RAVE survey and the newly calculated escape velocity by Monari et al. using Gaia data. Our study finds that the astrophysical uncertainties are dominated by the uncertainty in the escape velocity (independent of the best fit value) at dark matter masses below 6 GeV and can lead to a variation of nearly 6 orders of magnitude in the exclusion limits at 4 GeV. Above a WIMP mass of 6 GeV, the uncertainty becomes dominated by the local dark matter density, leading to uncertainties of factors of ∼10 (3) at 6 (15) GeV WIMP mass in the exclusion limits. Additionally, this work finds that the updated best fit value for the escape velocity based on Gaia data leads to only very minor changes to the effects of the astrophysical uncertainties on the XENON1T exclusion limits.

Author

Youjia Wu (University of Michigan)

Co-authors

Katherine Freese (University of Michigan) Chris Kelso (University of North Florida) Dr Patrick Stengel (Stockholm University) Monica Valluri (University of Michigan)

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