Cosmic-kite: Auto-encoding the cosmic microwave background

• Autor(a) principal: De Los Rios, Martín [UNESP]
• Data de Publicação: 2022
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1093/mnras/stac393; http://hdl.handle.net/11449/230665
• topic: Cosmic background radiation; Methods: data analysis; Methods: statistical

In this work, we present the results of the study of the cosmic microwave background temperatureerature 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 of 80 000 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 per cent to ≈ 0.2 per cent (depending on the cosmological parameter), while the decoder computes the power spectra with a mean percentage error of ≈ 0.0018 per cent 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, which 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.