维拉·C·鲁宾天文台宇宙剪切的点扩散函数（PSF）建模模拟测试
Simulation Tests of PSF Modeling for Cosmic Shear with the Vera C. Rubin Observatory
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中文总结 AI 辅助
本研究通过半真实图像模拟测试了PIFF包在类LSST数据上的PSF建模性能，发现其对非层析宇宙剪切的加性偏差贡献远低于宇宙剪切不确定度的30%，为LSST弱引力透镜分析的PSF验证提供了早期基准。
中文摘要 AI 辅助
为了通过下一代星系成像巡天（如维拉·C·鲁宾天文台的时空遗产巡天（LSST））实现无偏的宇宙剪切两点相关函数测量，需要对系统效应进行严格控制。一个关键挑战是准确建模点扩散函数（PSF），因为PSF估计中的误差会在星系形状测量中引入空间相关的偏差。将用于处理鲁宾数据的LSST科学管道包含了PIFF（全视场中的PSF）包的实现，该包最初为暗能量调查（DES）的Y3和Y6数据开发，且已被证明在这些数据上表现良好。在这项工作中，我们使用半真实图像模拟（模拟LSST在i波段、100平方度天区的观测条件）对鲁宾PSF建模管道进行端到端测试。我们使用二阶和四阶矩参数量化PSF模型残差，并通过一系列诊断测试对LSST第一年和第十年深度的性能进行评估。我们发现，PSF建模误差对非层析宇宙剪切数据向量的加性偏差贡献，远低于我们从解析协方差矩阵估计的宇宙剪切不确定度的30%。尽管我们的模拟排除了一些可能进一步挑战PSF建模的已知效应，但这些结果证明了PIFF在类LSST数据上的良好性能，并为正在进行的LSST弱引力透镜分析的PSF验证工作提供了早期基准。
英文摘要
Exceptional control of systematic effects is required in order to achieve unbiased cosmic shear two-point correlation function measurements with the next generation of galaxy imaging surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). One critical challenge is accurately modeling the point-spread function (PSF), as errors in PSF estimation can introduce spatially correlated biases in galaxy shape measurements. The LSST Science Pipelines, which will be used to process Rubin data, include an implementation of the PIFF (PSFs in the Full Field of View) package originally developed for, and demonstrated to perform well on, DES-Y3 and Y6 data. In this work we use semi-realistic image simulations, mimicking LSST observing conditions in $i$-band over 100 square degrees, to perform an end-to-end test of the Rubin PSF modeling pipeline. PSF model residuals are quantified using both second- and fourth-order moment parameters and performance is evaluated, for both LSST year 1 and year 10 depth, with a series of diagnostic tests. We find that the additive bias contribution from PSF modeling errors to the non-tomographic cosmic shear data vector is well below 30% of the cosmic shear uncertainty estimated from our analytic covariance matrix. Though our simulations exclude several known effects that may further challenge PSF modeling, these results demonstrate promising performance from PIFF on LSST-like data and provide an early benchmark for ongoing PSF validation efforts for LSST weak lensing analyses.
