KMS National Astronomical Observatories, CAS
| Baryon acoustic oscillations reconstruction using convolutional neural networks | |
| Mao, Tian-Xiang1,2; Wang, Jie1,2; Li, Baojiu3; Cai, Yan-Chuan4; Falck, Bridget5; Neyrinck, Mark6,7; Szalay, Alex5 | |
| 2021-02-01 | |
| Source Publication | MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
 |
| ISSN | 0035-8711 |
| Volume | 501Issue:1Pages: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 per cent at k <=Psi 0.2 hMpc(-1), which can lead to significant improvements of the BAO signal-to-noise ratio down to k similar or equal to 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. |
| Keyword | cosmological parameters dark energy large-scale structure of Universe |
| Funding Organization | National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Astronomical Big Data Joint Research Center ; Astronomical Big Data Joint Research Center ; National Astronomical Observatories, Chinese Academy of Sciences ; National Astronomical Observatories, Chinese Academy of Sciences ; Alibaba Cloud ; Alibaba Cloud ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Astronomical Big Data Joint Research Center ; Astronomical Big Data Joint Research Center ; National Astronomical Observatories, Chinese Academy of Sciences ; National Astronomical Observatories, Chinese Academy of Sciences ; Alibaba Cloud ; Alibaba Cloud ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Astronomical Big Data Joint Research Center ; Astronomical Big Data Joint Research Center ; National Astronomical Observatories, Chinese Academy of Sciences ; National Astronomical Observatories, Chinese Academy of Sciences ; Alibaba Cloud ; Alibaba Cloud ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Astronomical Big Data Joint Research Center ; Astronomical Big Data Joint Research Center ; National Astronomical Observatories, Chinese Academy of Sciences ; National Astronomical Observatories, Chinese Academy of Sciences ; Alibaba Cloud ; Alibaba Cloud |
| DOI | 10.1093/mnras/staa3741 |
| WOS Keyword | LARGE-SCALE STRUCTURE ; COSMIC DENSITY FIELD ; DARK ENERGY SURVEY ; ITERATIVE RECONSTRUCTION ; DISTANCE MEASUREMENTS ; INITIAL CONDITIONS ; GALAXIES ; I. ; PERTURBATION ; SIMULATIONS |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China (NSFC)[11873051] ; Astronomical Big Data Joint Research Center ; National Astronomical Observatories, Chinese Academy of Sciences ; Alibaba Cloud |
| Funding Organization | National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Astronomical Big Data Joint Research Center ; Astronomical Big Data Joint Research Center ; National Astronomical Observatories, Chinese Academy of Sciences ; National Astronomical Observatories, Chinese Academy of Sciences ; Alibaba Cloud ; Alibaba Cloud ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Astronomical Big Data Joint Research Center ; Astronomical Big Data Joint Research Center ; National Astronomical Observatories, Chinese Academy of Sciences ; National Astronomical Observatories, Chinese Academy of Sciences ; Alibaba Cloud ; Alibaba Cloud ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Astronomical Big Data Joint Research Center ; Astronomical Big Data Joint Research Center ; National Astronomical Observatories, Chinese Academy of Sciences ; National Astronomical Observatories, Chinese Academy of Sciences ; Alibaba Cloud ; Alibaba Cloud ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Astronomical Big Data Joint Research Center ; Astronomical Big Data Joint Research Center ; National Astronomical Observatories, Chinese Academy of Sciences ; National Astronomical Observatories, Chinese Academy of Sciences ; Alibaba Cloud ; Alibaba Cloud |
| WOS Research Area | Astronomy & Astrophysics |
| WOS Subject | Astronomy & Astrophysics |
| WOS ID | WOS:000608474800112 |
| Publisher | OXFORD UNIV PRESS |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.bao.ac.cn/handle/114a11/79761 |
| Collection | 中国科学院国家天文台 |
| Corresponding Author | Mao, Tian-Xiang |
| Affiliation | 1.Chinese Acad Sci, Natl Astron Observ, Beijing 100012, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Univ Durham, Inst Computat Cosmol, Dept Phys, Durham DH1 3LE, England 4.Univ Edinburgh, Inst Astron, Royal Observ, Blackford Hill, Edinburgh EH9 3HJ, Midlothian, Scotland 5.Johns Hopkins Univ, Dept Phys & Astron, Baltimore, MD 21218 USA 6.Basque Fdn Sci, Ikerbasque, E-48080 Bilbao, Spain 7.Univ Basque Country, Dept Phys, E-48080 Bilbao, Spain |
| Recommended Citation GB/T 7714 | Mao, Tian-Xiang,Wang, Jie,Li, Baojiu,et al. Baryon acoustic oscillations reconstruction using convolutional neural networks[J]. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,2021,501(1):1499-1510. |
| APA | Mao, Tian-Xiang.,Wang, Jie.,Li, Baojiu.,Cai, Yan-Chuan.,Falck, Bridget.,...&Szalay, Alex.(2021).Baryon acoustic oscillations reconstruction using convolutional neural networks.MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,501(1),1499-1510. |
| MLA | Mao, Tian-Xiang,et al."Baryon acoustic oscillations reconstruction using convolutional neural networks".MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 501.1(2021):1499-1510. |
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