Baryon acoustic oscillations reconstruction using convolutional neural networks. Issue 1 (5th December 2020)

• Record Type: Journal Article
• Authors: Mao, Tian-Xiang; Wang, Jie; Li, Baojiu; Cai, Yan-Chuan; Falck, Bridget; Neyrinck, Mark; Szalay, Alex
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 501:Issue 1(2021)
• Page Start: 1499
• Page End: 1510
• Publication Date: 2020-12-05
• Subjects: cosmological parameters -- dark energy -- large-scale structure of Universe; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/staa3741
• Languages: English
• ISSNs: 0035-8711

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.