Cosmic Kite: Auto-encoding the Cosmic Microwave Background

TL;DR

This paper introduces Cosmic Kite, an auto-encoder-based method that efficiently estimates cosmological parameters and generates power spectra from the cosmic microwave background data, enabling faster Bayesian analysis.

Contribution

It presents a novel auto-encoder architecture with latent variables representing cosmological parameters, providing a fast and interpretable forward model for CMB power spectrum analysis.

Findings

Encoder estimates parameters with 0.004% to 0.2% precision.

Decoder computes power spectra with 0.0018% mean error.

Method reduces computation time significantly.

Abstract

In this work we present the results of the study of the cosmic microwave background TT power spectrum through auto-encoders in which the latent variables are the cosmological parameters. This method was trained and calibrated using a data-set composed by 80000 power spectra from random cosmologies computed numerically with the CAMB code. Due to the specific architecture of the auto-encoder, the encoder part is a model that estimates the maximum-likelihood parameters from a given power spectrum. On the other hand, the decoder part is a model that computes the power spectrum from the cosmological parameters and can be used as a forward model in a fully Bayesian analysis. We show that the encoder is able to estimate the true cosmological parameters with a precision varying from $\approx 0.004\% $ to $\approx 0.2\% $ (depending on the cosmological parameter), while the decoder computes the power spectra with a mean percentage error of $\approx 0.0018\% $ for all the multipole range. We also demonstrate that the decoder recovers the expected trends when varying the cosmological parameters one by one, and that it does not introduce any significant bias on the estimation of cosmological parameters through a Bayesian analysis. These studies gave place to the Cosmic Kite python software that is publicly available and can be downloaded and installed from https://github.com/Martindelosrios/cosmic-kite. Although this algorithm does not improve the precision of the measurements compared with the traditional methods, it reduces significantly the computation time and represents the first attempt towards forcing the latent variables to have a physical interpretation.