Mass classification of dark matter perturbers of stellar tidal streams

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Francesco Montanari, J. García-Bellido

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. As an application, we forecast model selection strength of evidence for cold dark matter clusters of masses 103-105M⊙, 105-107M⊙ and 107-109M⊙, 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 the primordial black holes cold dark matter hypothesis. We expect moderate to strong evidence for model selection based on the PDF analysis when assuming low and intermediate dark matter perturbers mass ranges as fiducial models, but only weak evidence when the larger mass range is taken to be the fiducial model. Instead, the gradient boosting model is a highly efficient classifier (F1-scores larger than 90%) for all mass ranges here considered.

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