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Baryon acoustic oscillations reconstruction using convolutional neural networks

Mao, Tian-Xiang and Wang, Jie and Li, Baojiu and Cai, Yan-Chuan and Falck, Bridget and Neyrinck, Mark and Szalay, Alex (2021) 'Baryon acoustic oscillations reconstruction using convolutional neural networks.', Monthly notices of the Royal Astronomical Society, 501 (1). pp. 1499-1510.

Abstract

We propose a new scheme to reconstruct the baryon acoustic oscillations (BAO) signal, which contains key cosmological information, based on deep convolutional neural networks (CNN). Trained with almost no fine tuning, the network can recover large-scale modes accurately in the test set: the correlation coefficient between the true and reconstructed initial conditions reaches 90% at k ≤ 0.2 hMpc−1, which can lead to significant improvements of the BAO signal-to-noise ratio down to k ≃ 0.4 hMpc−1. Since this new scheme is based on the configuration-space density field in sub-boxes, it is local and less affected by survey boundaries than the standard reconstruction method, as our tests confirm. We find that the network trained in one cosmology is able to reconstruct BAO peaks in the others, i.e. recovering information lost to non-linearity independent of cosmology. The accuracy of recovered BAO peak positions is far less than that caused by the difference in the cosmology models for training and testing, suggesting that different models can be distinguished efficiently in our scheme. It is very promising that Our scheme provides a different new way to extract the cosmological information from the ongoing and future large galaxy surveys.

Item Type:Article
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1093/mnras/staa3741
Publisher statement:This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2020 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved
Date accepted:25 November 2020
Date deposited:15 July 2021
Date of first online publication:05 December 2020
Date first made open access:15 July 2021

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