Baryon acoustic oscillations reconstruction using convolutional neural networks

Author:

Mao Tian-Xiang12ORCID,Wang Jie12,Li Baojiu3ORCID,Cai Yan-Chuan4,Falck Bridget5,Neyrinck Mark6ORCID,Szalay Alex5

Affiliation:

1. National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China

2. University of Chinese Academy of Sciences, Beijing 100049, China

3. Institute for Computational Cosmology, Department of Physics, Durham University, Durham DH1 3LE, UK

4. Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK

5. Department of Physics and Astronomy, The Johns Hopkins University, Baltimore, MD 21218, USA

6. Ikerbasque, the Basque Foundation for Science and Department of Physics, University of the Basque Country, E-48080 Bilbao, Spain

Abstract

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 $90{{\ \rm per\ cent}}$ at $k\le 0.2 \, h\mathrm{Mpc}^{-1}$, which can lead to significant improvements of the BAO signal-to-noise ratio down to $k\simeq 0.4\, h\mathrm{Mpc}^{-1}$. 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.

Funder

National Natural Science Foundation of China

Chinese Academy of Sciences

Publisher

Oxford University Press (OUP)

Subject

Space and Planetary Science,Astronomy and Astrophysics

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