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Digvijay Wadekar (New York University) “Accelerating analysis of cosmological surveys with novel analytic methods and machine learning”

January 12, 2021 @ 4:00 pm - 5:00 pm

– Abstract:

In order to infer cosmological parameters from data of upcoming surveys like DESI, we will use summary statistics such as the power spectrum. An accurate estimate of their covariance matrix is crucial for the inference analysis. The traditional process of obtaining the covariance involves thousands of expensive mock simulations. I will present a novel analytic approach to calculate the covariance which is faster by more than four orders of magnitude. I will demonstrate the validation of our analytic approach with an analysis of the BOSS DR12 data and also show that it helps reduce the systematic error budget of cosmological constraints, thus making our approach useful for upcoming high-precision surveys like the Rubin observatory and DESI.

In the second part of my talk, I will present machine learning methods to emulate expensive hydrodynamical simulations and make cheap and accurate synthetic catalogs, which are needed for modeling non-linear scales in galaxy and line intensity mapping surveys.

http://physics.lbl.gov/rpm/index.php/events/

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Details

Date:: January 12, 2021
Time:: 4:00 pm - 5:00 pm
Website:: https://lbnl.zoom.us/j/99887141178

Venue

: Zoom Talk
: 50A-5132
Berkeley, ca 94720 + Google Map