Baryon acoustic oscillations reconstruction using convolutional neural networks

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

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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 per cent at k≤0.2hMpc−1⁠, which can lead to significant improvements of the BAO signal-to-noise ratio down to k≃0.4hMpc−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.
Original languageEnglish
Pages (from-to)1499-1510
Number of pages12
JournalMonthly Notices of the Royal Astronomical Society
Issue number1
Early online date5 Dec 2020
Publication statusPublished - 1 Feb 2021

Keywords / Materials (for Non-textual outputs)

  • astro-ph.CO
  • cs.LG


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