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Extreme data compression for the CMB

Journal Article · · Physical Review D
 [1];  [2]
  1. Univ. of Chicago, Chicago, IL (United States)
  2. Univ. of Chicago, Chicago, IL (United States); Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
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We apply the Karhunen-Loéve methods to cosmic microwave background (CMB) data sets, and show that we can recover the input cosmology and obtain the marginalized likelihoods in Λ cold dark matter cosmologies in under a minute, much faster than Markov chain Monte Carlo methods. This is achieved by forming a linear combination of the power spectra at each multipole l, and solving a system of simultaneous equations such that the Fisher matrix is locally unchanged. Instead of carrying out a full likelihood evaluation over the whole parameter space, we need evaluate the likelihood only for the parameter of interest, with the data compression effectively marginalizing over all other parameters. The weighting vectors contain insight about the physical effects of the parameters on the CMB anisotropy power spectrum Cl. The shape and amplitude of these vectors give an intuitive feel for the physics of the CMB, the sensitivity of the observed spectrum to cosmological parameters, and the relative sensitivity of different experiments to cosmological parameters. We test this method on exact theory Cl as well as on a Wilkinson Microwave Anisotropy Probe (WMAP)-like CMB data set generated from a random realization of a fiducial cosmology, comparing the compression results to those from a full likelihood analysis using CosmoMC. Furthermore, after showing that the method works, we apply it to the temperature power spectrum from the WMAP seven-year data release, and discuss the successes and limitations of our method as applied to a real data set.
Research Organization:
Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
Sponsoring Organization:
USDOE; USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
Grant/Contract Number:
AC02-07CH11359; SC0009924
OSTI ID:
1253181
Alternate ID(s):
OSTI ID: 1249799
Report Number(s):
FERMILAB-PUB-15-542-A; arXiv:1512.00072; 1407435
Journal Information:
Physical Review D, Journal Name: Physical Review D Journal Issue: 8 Vol. 93; ISSN PRVDAQ; ISSN 2470-0010
Publisher:
American Physical Society (APS)Copyright Statement
Country of Publication:
United States
Language:
English

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Cited By (9)

Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology journal March 2018
Enhancing BOSS bispectrum cosmological constraints with maximal compression journal January 2019
Nuisance hardened data compression for fast likelihood-free inference journal July 2019
Generalized massive optimal data compression journal February 2018
Data compression in cosmology: A compressed likelihood for Planck data journal October 2019
Automatic physical inference with information maximizing neural networks journal April 2018
Parameter constraints from cross-correlation of CMB lensing with galaxy clustering journal June 2018
Parameter constraints from cross-correlation of CMB lensing with galaxy clustering text January 2017
Nuisance hardened data compression for fast likelihood-free inference text January 2019

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