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PRODID:UW-Madison-Physics-Events
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SEQUENCE:0
UID:UW-Physics-Event-9700
DTSTART:20260622T160000Z
DTEND:20260622T180000Z
DTSTAMP:20260513T145951Z
LAST-MODIFIED:20260508T191539Z
LOCATION:Chamberlin 5290
SUMMARY:Precision Cosmology from Cross-Correlations of the Cosmic Micr
 owave Background and Large-Scale Structure\, Graduate Program Event\, 
 Yurii Kvasiuk
DESCRIPTION:Cross-correlations between the Cosmic Microwave Background
  (CMB) and the large-scale structure (LSS) of the Universe provide a p
 owerful avenue for extracting cosmological information. This thesis de
 velops new statistical and machine-learning methods for analyzing CMBÃ
 —LSS cross-correlations more efficiently\, with a particular focus on 
 the kinetic Sunyaev--Zel'dovich (kSZ) effect and local primordial non-
 Gaussianity. I introduce an optimal Bayesian framework for cosmic velo
 city reconstruction based on autodifferentiable likelihoods. I then pr
 esent machine-learning methods for modeling the electron distribution 
 and for estimating non-Gaussianity from LSS surveys. In addition\, I d
 evelop an optimal method for large-scale power spectrum estimation in 
 tomographic redshift surveys. I conclude by discussing how these metho
 ds can be combined to achieve state-of-the-art precision in constraini
 ng primordial non-Gaussianity using data from current CMB and LSS surv
 eys
URL:https://www.physics.wisc.edu/events/?id=9700
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