arXivDaily arXiv每日学术速递 周一至周五更新
北美退伍军人节(Veterans Day),12 日无更新
arXiv 1909.02005astro-ph.COastro-ph.HEastro-ph.IMhep-phstat.ML
收藏

挖掘暗物质子结构:利用机器学习从强透镜推断次晕人口属性

Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning

Johann Brehmer, Siddharth Mishra-Sharma, Joeri Hermans, Gilles Louppe, Kyle Cranmer

摘要:

在强透镜系统中,暗物质亚结构在延伸弧上的微妙而独特的印记包含了大量关于小尺度上暗物质的性质和分布的信息,因此,也包含了关于底层粒子物理学的信息。然而,梳理出这种效果构成了一个重大的挑战,因为人口水平参数的现实模拟的似然函数是棘手的。我们应用最近开发的模拟为基础的推理技术的子结构推理的问题,在星系星系强透镜。通过利用从模拟器中提取的附加信息,神经网络被有效地训练以估计与表征子结构的总体水平参数相关联的似然比。通过对模拟数据的原理验证应用,我们表明,这些方法可以提供一种有效的和原则性的方式来同时分析强透镜的合奏,并可用于挖掘大样本的透镜图像交付不久的将来调查签名的暗物质子结构。

英文摘要:

The subtle and unique imprint of dark matter substructure on extended arcs in strong lensing systems contains a wealth of information about the properties and distribution of dark matter on small scales and, consequently, about the underlying particle physics. However, teasing out this effect poses a significant challenge since the likelihood function for realistic simulations of population-level parameters is intractable. We apply recently-developed simulation-based inference techniques to the problem of substructure inference in galaxy-galaxy strong lenses. By leveraging additional information extracted from the simulator, neural networks are efficiently trained to estimate likelihood ratios associated with population-level parameters characterizing substructure. Through proof-of-principle application to simulated data, we show that these methods can provide an efficient and principled way to simultaneously analyze an ensemble of strong lenses, and can be used to mine the large sample of lensing images deliverable by near-future surveys for signatures of dark matter substructure.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑