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arXiv 2408.06337astro-ph.COastro-ph.IM
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用于精确弱引力透镜剪切推断的解析噪声偏差校正

Analytical Noise Bias Correction for Precise Weak Lensing Shear Inference

Xiangchong Li, Rachel Mandelbaum, The LSST Dark Energy Science Collaboration

AI总结:

本文在 AnaCal 框架中实现并改进基于 Metacalibration 再加噪思想的解析噪声偏差校正,使 Rubin LSST 模拟中的弱透镜剪切乘性偏差低于千分之几且单星系处理快于 1 毫秒。

摘要:

噪声偏差是弱引力透镜测量中系统误差的重要来源,必须予以校正,以满足精确宇宙学时代现代成像巡天的严格标准。本文回顾了解析噪声偏差校正方法,并给出解析推导,证明利用 Metacalibration 引入的“再加噪(renoising)”噪声偏差校正方法,可以将剪切恢复至其二阶。我们在 AnaCal 剪切估计框架中实现了这种解析噪声偏差校正,并对噪声偏差校正算法提出若干改进。我们使用旨在复现 Rubin LSST 成像数据的模拟来评估改进后的 AnaCal。这些模拟包含半真实的星系和恒星,并配有具有代表性的星等分布与银河系空间密度分布。我们在多种观测挑战下进行测试,包括宇宙射线、CCD 缺陷列、亮星饱和、溢漏光迹以及空间变化的点扩散函数。结果表明,弱引力透镜剪切恢复中的乘性偏差小于千分之几,无需来自外部图像模拟的校准即可满足 LSST DESC 的要求。此外,我们的算法实现了快速处理,处理单个星系的时间不到 1 毫秒。

英文摘要:

Noise bias is a significant source of systematic error in weak gravitational lensing measurements that must be corrected to satisfy the stringent standards of modern imaging surveys in the era of precision cosmology. This paper reviews the analytical noise bias correction method and provides analytical derivations demonstrating that we can recover shear to its second order using the 'renoising' noise bias correction approach introduced by Metacalibration. We implement this analytical noise bias correction within the AnaCal shear estimation framework and propose several enhancements to the noise bias correction algorithm. We evaluate the improved AnaCal using simulations designed to replicate Rubin LSST imaging data. These simulations feature semi-realistic galaxies and stars, complete with representative distributions of magnitudes and Galactic spatial density. We conduct tests under various observational challenges, including cosmic rays, defective CCD columns, bright star saturation, bleed trails, and spatially variable point spread functions. Our results indicate a multiplicative bias in weak lensing shear recovery of less than a few tenths of a percent, meeting LSST DESC requirements without requiring calibration from external image simulations. Additionally, our algorithm achieves rapid processing, handling one galaxy in less than a millisecond.

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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