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arXiv 2401.08910astro-ph.COastro-ph.IM
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利用基于神经模拟推断的宇宙微波背景透镜效应反演星系团质量

Inferring galaxy cluster masses from cosmic microwave background lensing with neural simulation based inference

Eric J. Baxter, Shivam Pandey

AI总结:

本文提出用基于神经似然的模拟推断(SBI)从CMB透镜信号中约束星系团质量,证明其在可计算精确似然时接近最优,在不可行场景下仍能给出无偏估计,为未来宇宙巡天提供有力工具。

摘要:

大质量星系团产生的引力透镜效应会在角分尺度上扭曲观测到的宇宙微波背景(CMB),这些扭曲携带了星系团质量的信息。从CMB星系团透镜信号中提取星系团质量约束的标准方法要么不是最优的,要么忽略了重要的物理或观测效应,要么在计算上不可行,要么需要额外工作才能将透镜测量结果转化为对星系团质量的约束。我们将基于模拟的推断(SBI)与神经似然模型应用于该问题。我们表明,在可以计算精确似然的情况下,SBI对星系团质量的约束与精确似然一致,证明SBI约束接近最优。在精确似然无法实际计算的场景中,SBI仍然能够恢复单个星系团质量的无偏估计以及来自多个星系团的联合约束。SBI将成为约束未来宇宙巡天探测到的星系团质量的有力工具。本文所述分析所使用的代码将公开发布。

英文摘要:

Gravitational lensing by massive galaxy clusters distorts the observed cosmic microwave background (CMB) on arcminute scales, and these distortions carry information about cluster masses. Standard approaches to extracting cluster mass constraints from the CMB cluster lensing signal are either sub-optimal, ignore important physical or observational effects, are computationally intractable, or require additional work to turn the lensing measurements into constraints on cluster masses. We apply simulation based inference (SBI) using neural likelihood models to the problem. We show that in circumstances where the exact likelihood can be computed, the SBI constraints on cluster masses are in agreement with the exact likelihood, demonstrating that the SBI constraints are close to optimal. In scenarios where the exact likelihood cannot be feasibly computed, SBI still recovers unbiased estimates of individual cluster masses and combined constraints from multiple clusters. SBI will be a powerful tool for constraining the masses of galaxy clusters detected by future cosmic surveys. Code to run the analyses presented here will be made publicly available.

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。

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