Data compression in cosmology: A compressed likelihood for Planck data

TL;DR

This paper introduces CosMOPED, a Python tool that compresses Planck 2015 temperature data, simplifying likelihood calculations while maintaining high accuracy for cosmological parameter estimation.

Contribution

It presents a novel data compression method applied to Planck data, enabling efficient and accurate likelihood approximation with significantly reduced data dimensions.

Findings

CosMOPED achieves consistent parameter estimates with the uncompressed likelihood.

The low-ell Planck temperature likelihood can be approximated by two Gaussian data points.

The method performs well for both standard and extended cosmological models.

Abstract

We apply the massively optimized parameter estimation and data compression technique (MOPED) to the public Planck 2015 temperature likelihood, reducing the dimensions of the data space to one number per parameter of interest. We present CosMOPED, a lightweight and convenient compressed likelihood code implemented in Python. In doing so we show that the $\ell<30$ Planck temperature likelihood can be well approximated by two Gaussian distributed data points, which allows us to replace the map-based low-$\ell$ temperature likelihood by a simple Gaussian likelihood. We make available a Python implementation of Planck's 2015 Plik_lite temperature likelihood that includes these low-$\ell$ binned temperature data (Planck-lite-py). We do not explicitly use the large-scale polarization data in CosMOPED, instead imposing a prior on the optical depth to reionization derived from these data. We show that the $\Lambda$CDM parameters recovered with CosMOPED are consistent with the uncompressed likelihood to within 0.1$\sigma$, and test that a 7-parameter extended model performs similarly well.