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CCAPP Seminar - Eduardo Rozo (University of Arizona)

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April 25, 2023
12:00PM - 1:00PM
PRB 4138

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Add to Calendar 2023-04-25 12:00:00 2023-04-25 13:00:00 CCAPP Seminar - Eduardo Rozo (University of Arizona) Speaker: Eduardo Rozo (University of Arizona) "A Field-Based Inference Approach to Cosmic Shear" All cosmological analyses to date rely on summary statistics: given a map of the survey data (e.g. a galaxy density map), one first computes a set of summary statistics (e.g. the correlation function), and then one proceeds to fit only that particular set of summary statistics. This approach is necessarily wasteful: some information is always lost in going from the survey map to the set of summary statistics under consideration. In this talk, I will describe a new approach referred to as field-based inference, in which one seeks to model not summary statistics, but the survey maps themselves. I will demonstrate field-based inference methods are expected to double the amount of information we can extract from current and future surveys, and discuss some of the challenges that remain to usher in a new era of field-based inference. PRB 4138 Center for Cosmology and AstroParticle Physics (CCAPP) ccapp@osu.edu America/New_York public

Speaker: Eduardo Rozo (University of Arizona)

"A Field-Based Inference Approach to Cosmic Shear"

All cosmological analyses to date rely on summary statistics: given a map of the survey data (e.g. a galaxy density map), one first computes a set of summary statistics (e.g. the correlation function), and then one proceeds to fit only that particular set of summary statistics. This approach is necessarily wasteful: some information is always lost in going from the survey map to the set of summary statistics under consideration. In this talk, I will describe a new approach referred to as field-based inference, in which one seeks to model not summary statistics, but the survey maps themselves. I will demonstrate field-based inference methods are expected to double the amount of information we can extract from current and future surveys, and discuss some of the challenges that remain to usher in a new era of field-based inference.

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