发表机构
University of British Columbia; Université Paris-Saclay, CNRS/IN2P3, IJCLab; Institute of Theoretical Astrophysics, University of Oslo（不列颠哥伦比亚大学; 巴黎萨克雷大学，法国国家科学研究中心/法国国家粒子物理研究所，IJCLab; 奥斯陆大学理论天体物理研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出HAWCS，一个基于healpy的Python包，用于高效计算全天球图的散射变换系数，以表征非高斯结构，并在普朗克观测和模拟的tSZ图及CMB透镜印记中验证了其有效性与实用性。
AI 中文摘要
从数据中提取高斯信息已被充分理解，但表征非高斯性仍具挑战性。我们引入HAWCS，一个基于healpy的Python包，用于高效计算全天球图的散射变换系数。这些系数提供了紧凑的汇总统计量，对跨角尺度的高阶结构和相互作用敏感。我们在受控的高斯和非高斯场上测试了该实现，并展示了其计算效率。随后，我们将HAWCS应用于由普朗克观测和四个模拟导出的热Sunyaev-Zeldovich图，量化并比较了它们的高阶统计特性。我们还使用该方法揭示了宇宙微波背景（CMB）温度图中引力透镜的印记。这些结果表明，HAWCS是验证分量分离流程和宇宙学模拟的实用工具，也是表征功率谱之外非高斯结构的有前景的汇总统计量。这些系数还可用于约束或生成模拟图，以再现真实数据的相同统计特征。
英文摘要
Extracting Gaussian information from data is well understood, but characterizing non-Gaussianity is challenging. We introduce HAWCS, a healpy-based Python package for efficiently computing wavelet scattering transform coefficients from full-sky maps. These coefficients provide compact summary statistics that are sensitive to higher-order structure and interactions across angular scales. We test the implementation on controlled Gaussian and non-Gaussian fields and demonstrate its computational efficiency. We then apply HAWCS to thermal Sunyaev-Zeldovich maps derived from Planck observations and four simulations, quantifying and comparing their higher-order statistical properties. We also use the method to expose the signatures of gravitational lensing in cosmic microwave background (CMB) temperature maps. These results demonstrate that HAWCS is a practical tool for validating component-separation pipelines and cosmological simulations, and a promising summary statistic for characterizing non-Gaussian structures beyond the power spectrum. These coefficients could also be used to constrain or generate simulated maps that reproduce the same statistical features of real data.
