发表机构
University of Crete; Foundation for Research and Technology Hellas (FORTH); Université Paris-Saclay; Université Paris Cité; CEA; CNRS（克里特大学; 希腊研究与技术基金会; 巴黎萨克雷大学; 巴黎西岱大学; 法国原子能与替代能源委员会; 法国国家科学研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究量化重子反馈对弱透镜高阶统计量宇宙学参数推断的偏置，提出尺度截断与BNT变换缓解策略，证明高阶统计量在无重子建模时仍优于功率谱。
AI 中文摘要
弱引力透镜是宇宙学探测的首要工具，但其在小尺度上的统计功效受到重子反馈的损害。高阶统计量捕捉了功率谱所遗漏的非高斯信息，然而它们对反馈的敏感性仍然是第四代（Stage IV）巡天所关注的问题。我们量化了未建模的反馈如何偏置从角功率谱（PS）、星小波峰值计数以及星小波 $\ell_1$-范数中推断出的宇宙学参数，并确定了消除该偏置所需的尺度截断。我们还测试了Bernardeau-Nishimichi-Taruya（BNT）变换作为更精确尺度截断的策略。我们的分析基于cosmoGRID V1套件，该套件通过重子修正模型在暗物质收敛图上印记反馈，并基于神经后验估计的模拟推断，覆盖从类第三阶段（Stage III）到全天区的巡天范围。我们发现偏置随巡天面积增大而增长，在类第四阶段巡天面积上，所有三种统计量的偏置均超过$2\sigma$，而消除这些偏置需牺牲相当一部分信号。限制在这些“重子安全”尺度上，星小波 $\ell_1$-范数的品质因数仍几乎是PS的两倍。BNT变换将重子敏感性定位到最低的变换红移bin，并将PS的品质因数提高了约1.4倍，而其形状噪声的线性混合则放大了基于地图的高阶统计量的等值线。因此，在完全不进行重子建模的情况下，高阶统计量相比功率谱提供了显著增益，并且随着建模的改进，增益会更大。
英文摘要
Weak gravitational lensing is a premier cosmological probe, but its small-scale statistical power is compromised by baryonic feedback. Higher-order statistics capture non-Gaussian information that the power spectrum misses, yet their sensitivity to feedback remains a concern for Stage IV surveys. We quantify how unmodeled feedback biases the cosmological parameters inferred from the angular power spectrum (PS), starlet peak counts, and the starlet $\ell_1$-norm, and we determine the scale cuts needed to remove that bias. We also test the Bernardeau-Nishimichi-Taruya (BNT) transform as a strategy for more precise scale cuts. Our analysis is based on the cosmoGRID V1 suite, which imprints feedback on dark matter convergence maps with a baryon correction model, and on simulation-based inference with neural posterior estimation, carried out across footprints ranging from Stage III-like to the full sky. We find that biases grow with survey area, exceeding $2σ$ for all three statistics at Stage IV-like footprints, and removing them costs a substantial fraction of the signal. Restricted to these ``baryon-safe'' scales, the starlet $\ell_1$-norm still reaches a figure of merit almost twice that of the PS. The BNT transform localizes the baryonic sensitivity to the lowest transformed redshift bin and improves the PS figure of merit by a factor of $\sim$1.4, while its linear mixing of shape noise inflates the contours of the map-based higher-order statistics. Higher-order statistics therefore deliver a substantial gain over the power spectrum with no baryonic modeling at all, and an even larger one as modeling improves.
