论文标题
通过具有神经似然比估计的强透镜观测值推断出subhalo的有效密度斜率
Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation
论文作者
论文摘要
强烈的引力镜头已成为一种有前途的方法，用于探测亚半乳酸尺度上的暗物质模型。最近的工作提出了Subhalo有效密度斜率比常用的Subhalo质量功能更可靠。 subhalo有效密度斜率是一个独立于对基本密度曲线的假设的测量值，可以通过传统的采样方法来推断单个Subhalos。为了超越单个Subhalo的测量，我们利用机器学习的最新进展，并引入神经似然比估计器来推断Subhalos人群的有效密度斜率。我们证明我们的方法能够利用多个Subhalos（内部和跨多个图像）的统计能力来区分不同的Subhalo种群的特征。神经可能性比率估计器对传统抽样的计算效率可以实现对暗物质渗透者的统计研究，并且特别有用，因为我们预计即将进行的调查会大量涌入强烈的镜头系统。
Strong gravitational lensing has emerged as a promising approach for probing dark matter models on sub-galactic scales. Recent work has proposed the subhalo effective density slope as a more reliable observable than the commonly used subhalo mass function. The subhalo effective density slope is a measurement independent of assumptions about the underlying density profile and can be inferred for individual subhalos through traditional sampling methods. To go beyond individual subhalo measurements, we leverage recent advances in machine learning and introduce a neural likelihood-ratio estimator to infer an effective density slope for populations of subhalos. We demonstrate that our method is capable of harnessing the statistical power of multiple subhalos (within and across multiple images) to distinguish between characteristics of different subhalo populations. The computational efficiency warranted by the neural likelihood-ratio estimator over traditional sampling enables statistical studies of dark matter perturbers and is particularly useful as we expect an influx of strong lensing systems from upcoming surveys.
