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Predicting the Expansion of Supernova Shells Using Deep Learning toward Highly Resolved Galaxy Simulations

Published online by Cambridge University Press:  20 January 2023

Keiya Hirashima Open the ORCID record for Keiya Hirashima [Opens in a new window] ,
Kana Moriwaki ,
Michiko Fujii Open the ORCID record for Michiko Fujii [Opens in a new window] ,
Yutaka Hirai ,
Takayuki Saitoh  and
Junichiro Makino
Keiya Hirashima*
Affiliation:
Department of Astronomy, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
Kana Moriwaki
Affiliation:
Department of Physics, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
Michiko Fujii
Affiliation:
Department of Astronomy, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
Yutaka Hirai
Affiliation:
Department of Physics, University of Notre Dame, 225 Nieuwland Science Hall, Notre Dame, IN 46556, USA Astronomical Institute, Tohoku University, 6-3, Aramaki, Aoba-ku, Sendai, Miyagi 980-8578, Japan RIKEN Center for Computational Science, 7-1-26 Minatojima-Minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
Takayuki Saitoh
Affiliation:
Department of Planetology, Graduate School of Science, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe, Hyogo 657-8501, Japan
Junichiro Makino
Affiliation:
RIKEN Center for Computational Science, 7-1-26 Minatojima-Minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan Department of Planetology, Graduate School of Science, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe, Hyogo 657-8501, Japan
*
†E-mail: [email protected]
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Abstract

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The load imbalance and communication overhead of parallel computing are crucial bottlenecks for galaxy simulations. A successful way to improve the scalability of astronomical simulations is a Hamiltonian splitting method, which needs to identify such regions integrated with smaller timesteps than the global timestep for integrating the entire galaxy. In the case of galaxy simulations, the regions inside supernova (SN) shells require the smallest steps. We developed the deep learning model to forecast the region affected by the SN shell’s expansion during one global step. In addition, we identified the particles with small timesteps using image processing. We can identify target particles using our method with a higher identification rate (88 % to 98 % on average) and lower “non-target”-to-“target” fraction (6.4 to 5.5 on average) compared to the analytic approach with the Sedov-Taylor solution. Our method using Hamiltonian splitting and deep learning will improve the performance of extremely high-resolution galaxy simulations.

Keywords

Deep LearningSupernovaGalaxy: formation
Type
Contributed Paper
Information
Proceedings of the International Astronomical Union , Volume 16 , Symposium S362: The Predictive Power of Computational Astrophysics as a Discovery Tool , June 2020 , pp. 209 - 214
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - SA
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike licence (https://creativecommons.org/licenses/by-nc-sa/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the same Creative Commons licence is included and the original work is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use.
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of International Astronomical Union

References

Fujii, M., Iwasawa, M., Funato, Y., & Makino, J. 2007, PASJ, 59, 1095.10.1093/pasj/59.6.1095CrossRefGoogle Scholar
Gingold, R. A. & Monaghan, J. J. 1977, MNRAS, 181, 375–389.10.1093/mnras/181.3.375CrossRefGoogle Scholar
Iwasawa, M., Tanikawa, A., Hosono, N., Nitadori, K., Muranushi, T., & Makino, J. 2016, PASJ, 68(4), 54.10.1093/pasj/psw053CrossRefGoogle Scholar
Lucy, L. B. 1977, AJ, 82, 1013–1024.10.1086/112164CrossRefGoogle Scholar
Pillepich, A., Springel, V., Nelson, D., Genel, S., Naiman, J., Pakmor, R., Hernquist, L., Torrey, P., Vogelsberger, M., Weinberger, R., & Marinacci, F. 2018, MNRAS, 473(3), 4077–4106.10.1093/mnras/stx2656CrossRefGoogle Scholar
Saitoh, T. R., Daisaka, H., Kokubo, E., Makino, J., Okamoto, T., Tomisaka, K., Wada, K., & Yoshida, N. 2008, PASJ, 60(4), 667–681.10.1093/pasj/60.4.667CrossRefGoogle Scholar
Sedov, L. I. 1959,.Google Scholar
Springel, V., Pakmor, R., Zier, O., & Reinecke, M. 2021, MNRAS, 506(2), 2871–2949.10.1093/mnras/stab1855CrossRefGoogle Scholar
Wang, Y., Zhang, J., Zhu, H., Long, M., Wang, J., & Yu, P. S. 2018, arXiv e-prints, arXiv:1811.07490.Google Scholar
Weinberger, R., Springel, V., Hernquist, L., Pillepich, A., Marinacci, F., Pakmor, R., Nelson, D., Genel, S., Vogelsberger, M., Naiman, J., & Torrey, P. 2017, MNRAS, 465(3), 3291–3308.10.1093/mnras/stw2944CrossRefGoogle Scholar