Machine learning transforms the inference of the nuclear equation of state

doi:10.1007/s11467-023-1313-3

Front. Phys.

2023, Vol. 18

Issue (6) : 64402 https://doi.org/10.1007/s11467-023-1313-3

VIEW & PERSPECTIVE

Machine learning transforms the inference of the nuclear equation of state

Yongjia Wang¹, Qingfeng Li^1,^2,³(

)

¹. School of Science, Huzhou University, Huzhou 313000, China
². Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China
³. School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China

Download: PDF(3007 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Our knowledge of the properties of dense nuclear matter is usually obtained indirectly via nuclear experiments, astrophysical observations, and nuclear theory calculations. Advancing our understanding of the nuclear equation of state (EOS, which is one of the most important properties and of central interest in nuclear physics) has relied on various data produced from experiments and calculations. We review how machine learning is revolutionizing the way we extract EOS from these data, and summarize the challenges and opportunities that come with the use of machine learning.

Keywords nuclear equation of state machine learning nuclear theory nuclear experiment

Corresponding Author(s): Qingfeng Li

About author:

^* These authors contributed equally to this work.

Issue Date: 21 June 2023

Cite this article:

Yongjia Wang,Qingfeng Li. Machine learning transforms the inference of the nuclear equation of state[J]. Front. Phys. , 2023, 18(6): 64402.

URL:

https://academic.hep.com.cn/fop/EN/10.1007/s11467-023-1313-3
https://academic.hep.com.cn/fop/EN/Y2023/V18/I6/64402

Fig.1 Conventional framework (dashed-line box) and machine learning based framework (solid-line box) in the study of nuclear physics.

1	Drischler C. , Holt J. , Wellenhofer C. . Chiral effective field theory and the high-density nuclear equation of state. Annu. Rev. Nucl. Part. Sci., 2021, 71(1): 403 https://doi.org/10.1146/annurev-nucl-102419-041903
2	A. Li B. , W. Chen L. , M. Ko C. . Recent progress and new challenges in isospin physics with heavy-ion reactions. Phys. Rep., 2008, 464(4−6): 113 https://doi.org/10.1016/j.physrep.2008.04.005
3	Oertel M. , Hempel M. , Klähn T. , Typel S. . Equations of state for supernovae and compact stars. Rev. Mod. Phys., 2017, 89(1): 015007 https://doi.org/10.1103/RevModPhys.89.015007
4	Sorensen A.Agarwal K.W. Brown K., et al.., Dense nuclear matter equation of state from heavy-ion collisions, arXiv: 2301.13253 (2023)
5	Lattimer J. . Neutron stars and the nuclear matter equation of state. Annu. Rev. Nucl. Part. Sci., 2021, 71(1): 433 https://doi.org/10.1146/annurev-nucl-102419-124827
6	A. López J. , O. Dorso C. , A. Frank G. . Properties of nuclear pastas. Front. Phys. (Beijing), 2021, 16(2): 24301 https://doi.org/10.1007/s11467-020-1004-2
7	Tsang M. , Stone J. , Camera F. , Danielewicz P. , Gandolfi S. , Hebeler K. , J. Horowitz C. , Lee J. , G. Lynch W. , Kohley Z. , Lemmon R. , Möller P. , Murakami T. , Riordan S. , Roca-Maza X. , Sammarruca F. , W. Steiner A. , Vidaña I. , J. Yennello S. . Constraints on the symmetry energy and neutron skins from experiments and theory. Phys. Rev. C, 2012, 86(1): 015803 https://doi.org/10.1103/PhysRevC.86.015803
8	Wolter H. , Colonna M. , Cozma D. . et al.. Transport model comparison studies of intermediate-energy heavy-ion collisions. Prog. Part. Nucl. Phys., 2022, 125: 103962 https://doi.org/10.1016/j.ppnp.2022.103962
9	J. Wang Y. , F. Li Q. . Application of microscopic transport model in the study of nuclear equation of state from heavy ion collisions at intermediate energies. Front. Phys. (Beijing), 2020, 15(4): 44302 https://doi.org/10.1007/s11467-020-0964-6
10	I. Jordan M. , M. Mitchell T. . Machine learning: Trends, perspectives, and prospects. Science, 2015, 349(6245): 255 https://doi.org/10.1126/science.aaa8415
11	Carleo G. , Cirac I. , Cranmer K. , Daudet L. , Schuld M. , Tishby N. , Vogt-Maranto L. , Zdeborová L. . Machine learning and the physical sciences. Rev. Mod. Phys., 2019, 91(4): 045002 https://doi.org/10.1103/RevModPhys.91.045002
12	J. Hey A.Tansley S.M. Tolle K., et al.., The Fourth Paradigm: Data-Intensive Scientific Discovery, Vol. 1, Microsoft research Redmond, WA, 2009
13	Gazula S. , Clark J. , Bohr H. . Learning and prediction of nuclear stability by neural networks. Nucl. Phys. A., 1992, 540(1−2): 1 https://doi.org/10.1016/0375-9474(92)90191-L
14	Bass S. , Bischoff A. , Hartnack C. , A. Maruhn J. , Reinhardt J. , Stocker H. , Greiner W. . Neural networks for impact parameter determination. J. Phys. G Nucl. Part. Phys., 1994, 20(1): L21 https://doi.org/10.1088/0954-3899/20/1/004
15	Bedaque P. , Boehnlein A. , Cromaz M. , Diefenthaler M. , Elouadrhiri L. , Horn T. , Kuchera M. , Lawrence D. , Lee D. , Lidia S. , McKeown R. , Melnitchouk W. , Nazarewicz W. , Orginos K. , Roblin Y. , Scott Smith M. , Schram M. , N. Wang X. . A. I. for nuclear physics. Eur. Phys. J. A, 2021, 57(3): 100 https://doi.org/10.1140/epja/s10050-020-00290-x
16	Boehnlein A. , Diefenthaler M. , Sato N. , Schram M. , Ziegler V. , Fanelli C. , Hjorth-Jensen M. , Horn T. , P. Kuchera M. , Lee D. , Nazarewicz W. , Ostroumov P. , Orginos K. , Poon A. , N. Wang X. , Scheinker A. , S. Smith M. , G. Pang L. . Colloquium: Machine learning in nuclear physics. Rev. Mod. Phys., 2022, 94(3): 031003 https://doi.org/10.1103/RevModPhys.94.031003
17	B. He W.G. Ma Y.G. Pang L.C. Song H.Zhou K., High energy nuclear physics meets Machine Learning, arXiv: 2303.06752 (2023)
18	He W.Li Q.Ma Y.M. Niu Z.C. Pei J.X. Zhang Y., Machine learning in nuclear physics at low and intermediate energies, arXiv: 2301.06396 (2023)
19	Morfouace P. , Tsang C. , Zhang Y. , G. Lynch W. , B. Tsang M. , D. S. Coupland D. , Youngs M. , Chajecki Z. , A. Famiano M. , K. Ghosh T. , Jhang G. , Lee J. , Liu H. , Sanetullaev A. , Showalter R. , Winkelbauer J. . Constraining the symmetry energy with heavy-ion collisions and Bayesian analyses. Phys. Lett. B, 2019, 799: 135045 https://doi.org/10.1016/j.physletb.2019.135045
20	O. Kuttan M.Steinheimer J.Zhou K.Stoecker H., The QCD EoS of dense nuclear matter from Bayesian analysis of heavy ion collision data, arXiv: 2211.11670 (2022)
21	A. Li B.J. Xie W., Bayesian inference of in-medium baryon−baryon scattering cross sections from HADES proton flow data, arXiv: 2303.10474 (2023)
22	Huth S. , T. Pang P. , Tews I. , Dietrich T. , Le Fèvre A. , Schwenk A. , Trautmann W. , Agarwal K. , Bulla M. , W. Coughlin M. , Van Den Broeck C. . Constraining neutron-star matter with microscopic and macroscopic collisions. Nature, 2022, 606(7913): 276 https://doi.org/10.1038/s41586-022-04750-w
23	G. Pang L. , Zhou K. , Su N. , Petersen H. , Stöcker H. , N. Wang X. . An equation-of-state-meter of quantum chromodynamics transition from deep learning. Nat. Commun., 2018, 9(1): 210 https://doi.org/10.1038/s41467-017-02726-3
24	Wang Y. , Gao Z. , Lü H. , Li Q. . Decoding the nuclear symmetry energy event-by-event in heavy-ion collisions with machine learning. Phys. Lett. B, 2022, 835: 137508 https://doi.org/10.1016/j.physletb.2022.137508
25	Zhou K.Wang L.G. Pang L.Z. Shi S., Exploring QCD matter in extreme conditions with Machine Learning, arXiv: 2303.15136 (2023)
26	Raissi M. , Perdikaris P. , E. Karniadakis G. . Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys., 2019, 378: 686 https://doi.org/10.1016/j.jcp.2018.10.045

[1]	Sue Sin Chong, Yi Sheng Ng, Hui-Qiong Wang, Jin-Cheng Zheng. Advances of machine learning in materials science: Ideas and techniques[J]. Front. Phys. , 2024, 19(1): 13501-.
[2]	Jinyu Wan, Yi Jiao. Machine learning-based direct solver for one-to-many problems on temporal shaping of relativistic electron beams[J]. Front. Phys. , 2022, 17(6): 64601-.
[3]	Cuiqian Yu, Yulou Ouyang, Jie Chen. Enhancing thermal transport in multilayer structures: A molecular dynamics study on Lennard−Jones solids[J]. Front. Phys. , 2022, 17(5): 53507-.
[4]	Daryl Ryan Chong, Minhyuk Kim, Jaewook Ahn, Heejeong Jeong. Machine learning identification of symmetrized base states of Rydberg atoms[J]. Front. Phys. , 2022, 17(1): 12504-.
[5]	Yulou Ouyang, Cuiqian Yu, Gang Yan, Jie Chen. Machine learning approach for the prediction and optimization of thermal transport properties[J]. Front. Phys. , 2021, 16(4): 43200-.
[6]	Wen Tong, Qun Wei, Hai-Yan Yan, Mei-Guang Zhang, Xuan-Min Zhu. Accelerating inverse crystal structure prediction by machine learning: A case study of carbon allotropes[J]. Front. Phys. , 2020, 15(6): 63501-.
[7]	Yong-Jia Wang, Qing-Feng Li. Application of microscopic transport model in the study of nuclear equation of state from heavy ion collisions at intermediate energies[J]. Front. Phys. , 2020, 15(4): 44302-.
[8]	Ce Wang, Hui Zhai. Machine learning of frustrated classical spin models (II): Kernel principal component analysis[J]. Front. Phys. , 2018, 13(5): 130507-.
[9]	CHEN Lie-wen, KO Che Ming, LI Bao-an, YONG Gao-chan. Probing the nuclear symmetry energy with heavy-ion reactions induced by neutron-rich nuclei[J]. Front. Phys. , 2007, 2(3): 327-357.

Viewed

Full text

Abstract

Cited

Shared

Discussed