Discretising the velocity distribution for directional dark matter experiments

TL;DR

This paper introduces an angular basis method for discretising the dark matter velocity distribution in directional detection experiments, enabling more robust, astrophysics-independent analysis of potential signals.

Contribution

The authors propose a positive-definite angular discretisation basis for the DM velocity distribution, facilitating accurate event rate calculations with minimal angular bins.

Findings

3 angular bins achieve 10-30% accuracy for smooth distributions

Discretisation error is larger for extreme velocity structures like streams

Method enables astrophysics-independent analysis of directional dark matter data

Abstract

Dark matter (DM) direct detection experiments which are directionally-sensitive may be the only method of probing the full velocity distribution function (VDF) of the Galactic DM halo. We present an angular basis for the DM VDF which can be used to parametrise the distribution in order to mitigate astrophysical uncertainties in future directional experiments and extract information about the DM halo. This basis consists of discretising the VDF in a series of angular bins, with the VDF being only a function of the DM speed $v$ within each bin. In contrast to other methods, such as spherical harmonic expansions, the use of this basis allows us to guarantee that the resulting VDF is everywhere positive and therefore physical. We present a recipe for calculating the event rates corresponding to the discrete VDF for an arbitrary number of angular bins $N$ and investigate the discretisation error which is introduced in this way. For smooth, Standard Halo Model-like distribution functions, only $N=3$ angular bins are required to achieve an accuracy of around $10-30\%$ in the number of events in each bin. Shortly after confirmation of the DM origin of the signal with around 50 events, this accuracy should be sufficient to allow the discretised velocity distribution to be employed reliably. For more extreme VDFs (such as streams), the discretisation error is typically much larger, but can be improved with increasing $N$. This method paves the way towards an astrophysics-independent analysis framework for the directional detection of dark matter.