Measurement of anisotropy of dark matter velocity distribution using directional detection

TL;DR

This study uses Monte Carlo simulations to analyze how directional detection can distinguish between isotropic and anisotropic dark matter velocity distributions, showing that with around 10,000 events, isotropy can be rejected with 90% confidence.

Contribution

The paper demonstrates the potential of directional detection to identify anisotropic components in dark matter velocity distributions using Monte Carlo simulations.

Findings

Isotropic distribution can be rejected at 90% confidence with ~10,000 events.

Directional detection provides a viable method to measure dark matter velocity anisotropy.

Simulation results support the sensitivity of directional detection to anisotropic components.

Abstract

Although the velocity distribution of dark matter is assumed to be generally isotropic, some studies have found that $\sim\hspace{-0.1cm}25$\% of the distribution can have anisotropic components. As the directional detection of dark matter is sensitive to both the recoil energy and direction of nuclear recoil, directional information can prove useful in measuring the distribution of dark matter. Using a Monte Carlo simulation based on the modeled directional detection of dark matter, we analyze the differences between isotropic and anisotropic distributions and show that the isotropic case can be rejected at a 90\% confidence level if $O(10^4)$ events can be obtained.