Reconstruction of the Density Power Spectrum from Quasar Spectra using Machine Learning

2021/07/19 by Maria Han Veiga, Veiga, Maria Han, X. M. Meng +7
Physics and Astronomy · #Galaxies: Formation, Evolution, Phenomena #Astronomy and Astrophysical Research #Cosmology and Gravitation Theories

paper · pdf · doi:10.48550/arxiv.2107.09082

Abstract

We describe a novel end-to-end approach using Machine Learning to reconstruct the power spectrum of cosmological density perturbations at high redshift from observed quasar spectra. State-of-the-art cosmological simulations of structure formation are used to generate a large synthetic dataset of line-of-sight absorption spectra paired with 1-dimensional fluid quantities along the same line-of-sight, such as the total density of matter and the density of neutral atomic hydrogen. With this dataset, we build a series of data-driven models to predict the power spectrum of total matter density. We are able to produce models which yield reconstruction to accuracy of about 1% for wavelengths k ≤ 2 h Mpc^-1, while the error increases at larger k. We show the size of data sample required to reach a particular error rate, giving a sense of how much data is necessary to reach a desired accuracy. This work provides a foundation for developing methods to analyse very large upcoming datasets with the next-generation observational facilities.

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