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(DarkAI) Mapping the large-scale density field of dark matter using artificial intelligence

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Abstract

Herein, we present a deep-learning technique for reconstructing the dark-matter density field from the redshift-space distribution of dark-matter halos. We built a UNet-architecture neural network and trained it using the COmoving Lagrangian Acceleration fast simulation, which is an approximation of the N-body simulation with 5123 particles in a box size of 500 h−1Mpc. Further, we tested the resulting UNet model not only with training-like test samples but also with standard N-body simulations, such as the Jiutian simulation with 61443 particles in a box size of 1000 h−1Mpc and the ELUCID simulation, which has a different cosmology. The real-space dark-matter density fields in the three simulations can be reconstructed reliably with only a small reduction of the cross-correlation power spectrum at 1% and 10% levels at k = 0.1 and 0.3 h Mpc−1, respectively. The reconstruction clearly helps to correct for redshift-space distortions and is unaffected by the different cosmologies between the training (Planck2018) and test samples (WMAP5). Furthermore, we tested the application of the UNet-reconstructed density field to obtain the velocity & tidal field and found that this approach provides better results compared with the traditional approach based on the linear bias model, showing a 12.2% improvement in the correlation slope and a 21.1% reduction in the scatter between the predicted and true velocities. Thus, our method is highly efficient and has excellent extrapolation reliability beyond the training set. This provides an ideal solution for determining the three-dimensional underlying density field from the plentiful galaxy survey data.

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Authors and Affiliations

  1. School of Aerospace Science and Technology, Xidian University, Xi’an, 710126, China

    Zitong Wang, Feng Shi, Yanming Liu & Xiaoping Li

  2. Department of Astronomy, School of Physics and Astronomy, and Shanghai Key Laboratory for Particle Physics and Cosmology, Shanghai Jiao Tong University, Shanghai, 200240, China

    Xiaohu Yang & Qingyang Li

  3. Tsung-Dao Lee Institute and Key Laboratory for Particle Physics, Astrophysics and Cosmology, Ministry of Education, Shanghai Jiao Tong University, Shanghai, 200240, China

    Xiaohu Yang

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

    Qingyang Li

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  1. Zitong Wang
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Conflict of interest The authors declare that they have no conflict of interest.

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This work was supported by the National SKA Program of China (Grant Nos. 2022SKA0110200, and 2022SKA0110202), National Natural Science Foundation of China (Grant Nos. 12103037, 11833005, and 11890692), 111 Project (Grant No. B20019), and Shanghai Natural Science Foundation (Grant No. 19ZR1466800). We acknowledged the Science Research grants from the China Manned Space Project (Grant No. CMS-CSST-2021-A02), and the Fundamental Research Funds for the Central Universities (Grant No. XJS221312). This work was supported by the High-Performance Computing Platform of Xidian University.

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Wang, Z., Shi, F., Yang, X. et al. (DarkAI) Mapping the large-scale density field of dark matter using artificial intelligence. Sci. China Phys. Mech. Astron. 67, 219513 (2024). https://doi.org/10.1007/s11433-023-2192-9

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