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Baryon acoustic oscillations reconstruction using convolutional neural networks

Mao, Tian-Xiang; Wang, Jie; Li, Baojiu; Cai, Yan-Chuan; Falck, Bridget; Neyrinck, Mark; Szalay, Alex

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Tian-Xiang Mao

Jie Wang

Yan-Chuan Cai

Bridget Falck

Mark Neyrinck

Alex Szalay


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.

Journal Article Type Article
Acceptance Date Nov 25, 2020
Online Publication Date Dec 5, 2020
Publication Date 2021-02
Deposit Date Feb 25, 2020
Publicly Available Date Jul 15, 2021
Journal Monthly Notices of the Royal Astronomical Society
Print ISSN 0035-8711
Electronic ISSN 1365-2966
Publisher Royal Astronomical Society
Peer Reviewed Peer Reviewed
Volume 501
Issue 1
Pages 1499-1510
Public URL
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Published Journal Article (2.2 Mb)

Copyright 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

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