Speaker
Description
Higher-order WL statistics can capture the non-Gaussian nature of the Cosmic Web and exploit that to offer stricter constraints on Cosmological/MG parameters. In this talk I will start by elaborating on the significance and advantages of looking into these higher-order statistics.
There are certain known systematic errors (like galaxy Intrinsic Alignment) that exist in WL data, which need to be accounted for to help the future Stage-IV surveys infer cosmological statistics and constraints effectively. IA is anticipated to significantly impact lensing statistics, and hence, is important to be accounted for.
My ongoing Mater’s thesis investigates the effect of infusing IA as a systematic to WL Maps generated with FORGE-BRIDGE, MGLenS using two-point and beyond two-point WL statistics for the f(R) and the nDGP MG models. I will proceed by presenting some results that I already have obtained and end by giving a glimpse into what is the aim of my project: The statistical measures are to be used to train a Gaussian Process Regression Emulator to reliably interpolate between IA, MG and Cosmological Parameters to provide physical insight wherever analytical models are not available, followed by MCMC analysis of the trained Emulator.
| Abstract title | Higher-order WL Statistics and Intrinsic Alignment |
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