Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning
Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning
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DOI：
10.3847/1538-4357/ab4c41
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发表时间：
2019-09
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通讯作者：
J. Brehmer;S. Mishra-Sharma;Joeri Hermans;Gilles Louppe;Kyle Cranmer
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作者：
J. Brehmer;S. Mishra-Sharma;Joeri Hermans;Gilles Louppe;Kyle Cranmer
在强透镜系统中，暗物质子结构在扩展弧上的微妙而独特的印记包含了大量关于暗物质在小尺度上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。我们最近开发的模拟为基础的推理技术的子结构推断在星系星系强透镜的问题。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和原则性的方法来同时分析强透镜的集合，并可用于挖掘近期调查可交付的大样本透镜图像，以获得暗物质子结构的签名。我们发现，在我们的简化建模框架内，分析一个样本的约100个镜头已经可以牵制的整体丰富的子结构内透镜星系的精度%与更大的灵敏度预期从一个更大的透镜样本。（https：//github.com/smSharma/StrongLensing-Inference）
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. We find that, within our simplified modeling framework, analyzing a sample of around 100 lenses can already pin down the overall abundance of substructure within lensing galaxies to a precision of % with greater sensitivity expected from a larger lens sample. (https://github.com/smsharma/StrongLensing-Inference)