论文标题
恒星潮汐流的暗物质腐败者的质量分类
Mass classification of dark matter perturbers of stellar tidal streams
论文作者
论文摘要
预计绕着银河系周围的祖细胞的潮汐剥离而形成的恒星流将受到与暗物质Subhalos的相遇的扰动。最近的研究表明，它们是推断Perturbers（例如质量）的特性的出色代理。在这里，我们提出了两种不同的方法论，它们利用了恒星流的完全非高斯密度分布：基于恒星密度的概率密度函数（PDF）的贝叶斯模型选择，以及无可能梯度的提升分类器。尽管计划不假定特定的暗物质模型，但我们主要有兴趣辨别原始黑洞冷暗物质（PBH CDM）假设形成了标准粒子暗物质。因此，作为应用程序，我们预测了质量的模型选择强度的质量证据$ 10^3 $ - $ 10^5 m _ {\ odot} $和$ 10^5 $ - $ 10^9 m _ {\ odot} $，基于GD -1类恒星流和包括现实的错误。对于PBH CDM而言，较小的质量范围的证据尤其有趣。我们期望根据PDF分析，根据基准模型，基于PDF分析的模型选择较弱。相反，对于此处考虑的所有质量范围，梯度提升模型是高效的分类器（99 \％精度）。作为该方法鲁棒性的进一步测试，我们在执行预测进一步将最大质量范围分为$ 10^5 $ -10^7 m _ {\ odot} $和$ 10^7 $ - $ 10^9 m _ {\ odot} $ ranges时得出了类似的结论。
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.
