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Activity Number: 314 - Statistical Challenges in Cosmology
Type: Topic-Contributed
Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #317580
Title: Cosmological Parameter Inference in Photometric Surveys: An Inverse Problem
Author(s): Markus Michael Rau*
Companies: Carnegie-Mellon-University
Keywords: Cosmology; Point Processes ; Inverse Problems; Approximate Inference; Big Data; Regularization

At the dawn of precision Cosmology, accurate control of systematic errors and efficient combination and exploration of complementary cosmological probes across surveys has become increasingly important. A dominant source of systematic error in cosmological data are uncertainties in the distance, or redshift, of galaxies. The `redshift' of galaxies parametrizes how the wavelength of light is `stretched' under an expanding background universe, and can be seen both as a measure of galaxy distance, and time. Thus, describing the galaxy positions as a Spatio-temporal point process, a fundamental challenge is to perform inference under uncertain, or latent, redshift measurements. I will describe both the physical and statistical relevance of this problem, focussing on problems of regularization, identifiability, likelihood construction, and scalability.

Authors who are presenting talks have a * after their name.

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