arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2002.10218astro-ph.COcs.LG
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Baryon acoustic oscillations reconstruction using convolutional neural networks

National Astronomical Observatories, Chinese Academy of Sciences(中国科学院国家天文台);University of Chinese Academy of Sciences(中国科学院大学);Durham University(杜伦大学);University of Edinburgh(爱丁堡大学);The Johns Hopkins University(约翰斯·霍普金斯大学);University of the Basque Country(巴斯克大学)

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

英文摘要:

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 $k\leq 0.2 h\mathrm{Mpc}^{-1}$, which can lead to significant improvements of the BAO signal-to-noise ratio down to $k\simeq0.4h\mathrm{Mpc}^{-1}$. 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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