Mass classification of dark matter perturbers of stellar tidal streams

TL;DR

This paper develops two novel methods to analyze stellar streams for dark matter subhalo properties, aiming to distinguish between primordial black holes and particle dark matter, with promising results in model selection and classification accuracy.

Contribution

Introduces Bayesian PDF-based model selection and a gradient boosting classifier to analyze stellar streams for dark matter subhalo mass, aiding in differentiating dark matter models.

Findings

Weak to strong evidence for model selection depending on the method and model.

Gradient boosting classifier achieves 99% accuracy across mass ranges.

Robust conclusions when subdividing large mass ranges.

Abstract

Stellar streams formed by tidal stripping of progenitors orbiting around the Milky Way are expected to be perturbed by encounters with dark matter subhalos. Recent studies have shown that they are an excellent proxy to infer properties of the perturbers, such as their mass. Here we present two different methodologies that make use of the fully non-Gaussian density distribution of stellar streams: a Bayesian model selection based on the probability density function (PDF) of stellar density, and a likelihood-free gradient boosting classifier. While the schemes do not assume a specific dark matter model, we are mainly interested in discerning the primordial black holes cold dark matter (PBH CDM) hypothesis form the standard particle dark matter one. Therefore, as an application we forecast model selection strength of evidence for cold dark matter clusters of masses $10^3$ - $10^5 M_{\odot}$ and $10^5$ - $10^9 M_{\odot}$, based on a GD-1-like stellar stream and including realistic observational errors. Evidence for the smaller mass range, so far under-explored, is particularly interesting for PBH CDM. We expect weak to strong evidence for model selection based on the PDF analysis, depending on the fiducial model. Instead, the gradient boosting model is a highly efficient classifier (99\% accuracy) for all mass ranges here considered. As a further test of the robustness of the method, we reach similar conclusions when performing forecasts further dividing the largest mass range into $10^5$ - $10^7 M_{\odot}$ and $10^7$ - $10^9 M_{\odot}$ ranges.