arXiv 2006.085612020-06astro-ph.COastro-ph.IM

一种利用散射变换进行观测宇宙学研究的新方法

A new approach to observational cosmology using the scattering transform

Sihao Cheng, Yuan-Sen Ting, Brice Ménard, Joan Bruna

备注：: 13 pages, 7 figures; accepted to MNRAS
发表信息：: Monthly Notices of the Royal Astronomical Society, Volume 499, Issue 4, 2020, pp. 5902-5914
DOI：: 10.1093/mnras/staa3165

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AI总结：

本文提出用无需训练的散射变换从非高斯场中提取紧凑可解释的统计系数，并在弱引力透镜模拟数据上证明其参数估计性能优于经典方法、与最先进CNN相当。

摘要：

对非高斯随机场进行参数估计是天体物理学和宇宙学中常见的挑战。在本文中，我们主张使用散射变换来完成这一任务，这是一种与卷积神经网络（CNN）共享思想但无需训练或调参的统计工具。它生成一组紧凑的系数，可用作非高斯信息的稳健汇总统计量。它特别适用于呈现局域化结构和层次聚类的场，例如宇宙学密度场。为了展示其能力，我们将该估计器应用于弱引力透镜背景下的宇宙学参数推断问题。在带有真实噪声的模拟收敛图上，散射变换优于经典估计器，并与最先进的CNN不相上下。它保留了传统统计描述符的优点，具有可证明的稳定性性质，允许检查系统误差，并且重要的是，散射系数是可解释的。对于观测宇宙学以及一般物理场的研究而言，它是一种强大且有吸引力的估计器。

英文摘要：

Parameter estimation with non-Gaussian stochastic fields is a common challenge in astrophysics and cosmology. In this paper, we advocate performing this task using the scattering transform, a statistical tool sharing ideas with convolutional neural networks (CNNs) but requiring no training nor tuning. It generates a compact set of coefficients, which can be used as robust summary statistics for non-Gaussian information. It is especially suited for fields presenting localized structures and hierarchical clustering, such as the cosmological density field. To demonstrate its power, we apply this estimator to a cosmological parameter inference problem in the context of weak lensing. On simulated convergence maps with realistic noise, the scattering transform outperforms classic estimators and is on a par with state-of-the-art CNN. It retains the advantages of traditional statistical descriptors, has provable stability properties, allows to check for systematics, and importantly, the scattering coefficients are interpretable. It is a powerful and attractive estimator for observational cosmology and the study of physical fields in general.

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