Extreme data compression for the CMB

TL;DR

This paper introduces a fast, data compression technique using Karhunen-Loève methods to efficiently analyze CMB data and estimate cosmological parameters, significantly reducing computation time.

Contribution

The authors develop a novel linear combination approach for CMB data that preserves Fisher matrix properties, enabling rapid likelihood evaluations and parameter inference.

Findings

Method accurately recovers input cosmology from simulated data.

Applied successfully to WMAP seven-year data set.

Achieves parameter estimation in under a minute.

Abstract

We apply the Karhunen-Lo\'eve methods to cosmic microwave background (CMB) data sets, and show that we can recover the input cosmology and obtain the marginalized likelihoods in $\Lambda$ 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 $C_l$. 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 $C_l$ 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. 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.