基于光度红移层析分箱的弱引力透镜剪切响应
Weak-Lensing Shear Response for Photometric Redshift-Based Tomographic Binning
- Brookhaven National Laboratory（布鲁海文国家实验室）
- University of Pittsburgh（匹兹堡大学）
- SLAC National Accelerator Laboratory（SLAC国家加速器实验室）
- Carnegie Mellon University（卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文扩展AnaCal框架，通过将剪切响应传播至选择过程，校正光度红移选择偏差，并在真实及模拟数据上验证，使乘性剪切偏差满足LSST十年要求。
AI中文摘要：
将源星系划分为层析红移分箱是现代弱引力透镜分析的基础，能够测量宇宙结构的增长和暗能量的性质。在实践中，这些层析分箱是使用光度红移（photo-$z$）估计来定义的。然而，光度红移估计与弱引力透镜剪切之间的相关性会在测量的剪切信号中引入与红移相关的选择偏差；如果不加以校正，这些偏差会扭曲透镜信号推断出的振幅和红移演化，进而使对宇宙时间尺度上宇宙结构增长的测量产生偏差。在本文中，我们将剪切测量的解析自校准（AnaCal）框架扩展，通过将剪切响应传播到选择过程中，来考虑基于光度红移的层析弱引力透镜分析中的选择偏差。这种方法无需外部图像模拟来校准此校正。作为对真实数据的首次合理性检查，我们在鲁宾天文台数据预览1数据集上验证了由AnaCal通量导出的光度红移估计，发现其达到的光度红移质量与标准LSST估计相当，在高红移处略优于后者。然后，我们在具有预期LSST Y10深度混合的类LSST图像模拟上，使用两种代表性的光度红移算法——模板拟合方法和机器学习方法——验证了剪切校准，结果表明由光度红移选择引起的乘性剪切偏差在两种算法的所有五个层析分箱中均保持在LSST十年要求$|m| < 3\ imes 10^{-3}$之内。这些结果确立了AnaCal作为即将到来的LSST分析中层析弱引力透镜科学的自洽流程。
英文摘要：
Dividing source galaxies into tomographic redshift bins is a cornerstone of modern weak gravitational lensing analyses, enabling measurements of the growth of cosmic structure and the nature of dark energy. In practice, these tomographic bins are defined using photometric redshift (photo-$z$) estimates. However, correlations between photo-$z$ estimates and weak lensing shear can introduce redshift-dependent selection biases in the measured shear signal; if left uncorrected, these biases distort the inferred amplitude and redshift evolution of the lensing signal, and in turn bias the measurement of the growth of cosmic structure across cosmic time. In this paper, we extend the analytical self-calibration for shear measurement (AnaCal) framework to account for photo-$z$-based selection biases in tomographic weak lensing analyses by propagating shear responses through the selection process. This approach eliminates the need for external image simulations to calibrate this correction. As a first sanity check on real data, we validate the photo-$z$ estimates derived from AnaCal fluxes on the Rubin Observatory Data Preview 1 dataset, and find that they reach photo-$z$ quality comparable to, and at high redshift slightly better than, the standard LSST estimates. We then validate the shear calibration on LSST-like image simulations with blending at the expected LSST Y10 depth, with two representative photo-$z$ algorithms -- a template-fitting method and a machine-learning method -- and show that the multiplicative shear bias induced by photo-$z$ selection remains within the LSST ten-year requirement $|m| < 3\times 10^{-3}$ across all five tomographic bins for both algorithms. These results establish AnaCal as a self-consistent pipeline for tomographic weak lensing science in upcoming LSST analyses.
