Mass classification of dark matter perturbers of stellar tidal streams. (arXiv:2012.11482v2 [astro-ph.GA] UPDATED)
<a href="http://arxiv.org/find/astro-ph/1/au:+Montanari_F/0/1/0/all/0/1">Francesco Montanari</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Garcia_Bellido_J/0/1/0/all/0/1">Juan Garc&#xed;a-Bellido</a>

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.

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.

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