Cosmic kite: Auto-encoding the Cosmic Microwave Background

DIPC Seminars

Martín de los Ríos, Instituto de Física Teórica (UAM), Spain
Donostia International Physics Center
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Cosmic kite: Auto-encoding the Cosmic Microwave Background Machine learning techniques represents a new way of analyzing big data-sets in an agnostic and homogeneous way. These methods are very useful and powerful tools to find patterns and relations between the variables that are involved in a specific problem. In this talk we will 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 ~0.004 % to ~0.2 %, while the decoder computes the power spectra with a mean percentage error of ~ 0.0018 % for all the multipole range. These studies gave place to the Cosmic Kite python software that is publicly available and can be downloaded and installed from Host: Marcos Pellejero ZOOM: YouTube: