Machine learning simulates collective flow and nuclear stopping in heavy-ion collisions at intermediate energies

[1] Agnieszka S, Kshitij A, Brown Kyle W et al. Dense nuclear matter equation of state from heavy-ion collisions[J]. Progress in Particle and Nuclear Physics, 134, 104080(2024).

[2] Wang J M, Deng X G, Xie W J et al. Bayesian inference of nuclear incompressibility from proton elliptic flow in central Au+Au collisions at 400 MeV/nucleon[EB/OL], 2406.07051(2024). https://arxiv.org/abs/2406.07051v1

[3] Wang Y J, Li Q F, Leifels Y et al. Study of the nuclear symmetry energy from the rapidity-dependent elliptic flow in heavy-ion collisions around 1 GeV/nucleon regime[J]. Physics Letters B, 802, 135249(2020).

[4] Xu J. Transport approaches for the description of intermediate-energy heavy-ion collisions[J]. Progress in Particle and Nuclear Physics, 106, 312-359(2019).

[5] Li B A, Ko C M, Bauer W. Isospin physics in heavy-ion collisions at intermediate energies[J]. International Journal of Modern Physics E, 7, 147-229(1998).

[6] Li H L, Xu H J, Zhou Y et al. Probing the neutron skin with ultrarelativistic isobaric collisions[J]. Physical Review Letters, 125, 222301(2020).

[7] Xu H J, Li H L, Zhou Y et al. Measuring neutron skin by grazing isobaric collisions[J]. Physical Review C, 105, L011901(2022).

[8] Omana Kuttan M, Steinheimer J, Zhou K et al. QCD equation of state of dense nuclear matter from a Bayesian analysis of heavy-ion collision data[J]. Physical Review Letters, 131, 202303(2023).

[9] Tsang C Y, Kurata-Nishimura M, Tsang M B et al. Constraining nucleon effective masses with flow and stopping observables from the SπRIT experiment[J]. Physics Letters B, 853, 138661(2024).

[10] Mäntysaari H, Schenke B, Shen C et al. Bayesian inference of the fluctuating proton shape[J]. Physics Letters B, 833, 137348(2022).

[11] Fan W, Vujanovic G, Bass S A et al. New metric improving Bayesian calibration of a multistage approach studying hadron and inclusive jet suppression[J]. Physical Review C, 109, 064903(2024).

[12] Everett D, Ke W, Paquet J F et al. Multisystem Bayesian constraints on the transport coefficients of QCD matter[J]. Physical Review C, 103, 054904(2021).

[13] Zhou J, Xu J. Bayesian inference of neutron-skin thickness and neutron-star observables based on effective nuclear interactions[J]. Science China Physics, Mechanics & Astronomy, 67, 282011(2024).

[14] Zhang Z, Chen L W. Bayesian inference of the symmetry energy and the neutron skin in 48Ca and 208Pb from CREX and PREX-2[J]. Physical Review C, 108, 024317(2023).

[15] Cox N, Grundler X, Li B A. Neural network emulation of flow in heavy-ion collisions at intermediate energies[J]. Physical Review C, 110, 044604(2024).

[16] Seeger M. Gaussian processes for machine learning[J]. International Journal of Neural Systems, 14, 69-106(2004).

[17] Boehnlein A, Diefenthaler M, Sato N et al. Colloquium: Machine learning in nuclear physics[J]. Reviews of Modern Physics, 94, 031003(2022).

[18] He W B, Li Q F, Ma Y G et al. Machine learning in nuclear physics at low and intermediate energies[J]. Science China Physics, Mechanics & Astronomy, 66, 282001(2023).

[19] Pang L G. Studying high-energy nuclear physics with machine learning[J]. International Journal of Modern Physics E, 33, 2430009(2024).

[20] LI Fupeng, PANG Longgang, WANG Xinnian. Application of machine learning to the study of QCD transition in heavy ion collisions[J]. Nuclear Techniques, 46, 040014(2023).

[21] GAO Zepeng, LI Qingfeng. Studies on several problems in nuclear physics by using machine learning[J]. Nuclear Techniques, 46, 080009(2023).

[22] ZHANG Yingxun, WANG Fangyuan, LI Li et al. Study of physics problem in low-intermediate energy heavy ion collision with machine learning algorithm[J]. Atomic Energy Science and Technolngy, 57, 751-761(2023).

[23] YI Jiayi, QIAO Chunyuan, PEI Junchen et al. Bayesian machine learning for the uncertainty evaluation of nuclear fission product yields[J]. Scientia Sinica Physica, Mechanica & Astronomica, 52, 252013(2022).

[24] HAN Shuochong, SHANG Xinle, ZUO Wei. Taming the in-medium nucleon-nucleon cross section with a deep neural network method[J]. Scientia Sinica (Physica, Mechanica & Astronomica), 52, 139-148(2022).

[25] Li F P, Wang Y J, Gao Z P et al. Application of machine learning in the determination of impact parameter in the 132Sn+124Sn system[EB/OL], 2105.08912(2021). https://arxiv.org/abs/2105.08912v1

[26] Saha A, Dan D, Sanyal S. Machine-learning model-driven prediction of the initial geometry in heavy-ion collision experiments[J]. Physical Review C, 106, 014901(2022).

[27] Zhang X, Liu X, Huang Y et al. Determining impact parameters of heavy-ion collisions at low-intermediate incident energies using deep learning with convolutional neural networks[J]. Physical Review C, 105, 034611(2022).

[28] Wang Y J, Gao Z P, Lü H L et al. Decoding the nuclear symmetry energy event-by-event in heavy-ion collisions with machine learning[J]. Physics Letters B, 835, 137508(2022).

[29] Huang Y G, Pang L G, Luo X F et al. Probing criticality with deep learning in relativistic heavy-ion collisions[J]. Physics Letters B, 827, 137001(2022).

[30] Wang Y J, Li Q F. Application of microscopic transport model in the study of nuclear equation of state from heavy ion collisions at intermediate energies[J]. Frontiers of Physics, 15, 44302(2020).

[31] Zhang Y X, Liu Y Y, Wang Y J et al. How do constraints of nuclear symmetry energy reconcile with different models?[EB/OL], 2301.00212(2022). https://arxiv.org/abs/2301.00212v1

[32] Li Q F, Wang Y J, Wang X B et al. Rapidity distribution of protons from the potential version of UrQMD model and the traditional coalescence afterburner[J]. Science China Physics, Mechanics & Astronomy, 59, 622001(2016).

[33] Liu Y Y, Wang Y J, Cui Y et al. Insights into the pion production mechanism and the symmetry energy at high density[J]. Physical Review C, 103, 014616(2021).

[34] Li P C, Wang Y J, Li Q F et al. Effects of the in-medium nucleon-nucleon cross section on collective flow and nuclear stopping in heavy-ion collisions in the Fermi-energy domain[J]. Physical Review C, 97, 044620(2018).

[35] Tong L Y, Li P C, Li F P et al. Nucleon effective mass splitting and density-dependent symmetry energy effects on elliptic flow in heavy ion collisions at Elab=0.09~1.5 GeV/nucleon[J]. Chinese Physics C, 44, 074103(2020).

[36] Xu H J, Wang X B, Li H L et al. Importance of isobar density distributions on the chiral magnetic effect search[J]. Physical Review Letters, 121, 022301(2018).

[37] Xu H J, Li Z P, Song H C. High-order flow harmonics of identified hadrons in 2.76 A TeV Pb+Pb collisions[J]. Physical Review C, 93, 064905(2016).

[38] Wang Y J, Guo C C, Li Q F et al. Determination of the nuclear incompressibility from the rapidity-dependent elliptic flow in heavy-ion collisions at beam energies 0.4~1.0 A GeV[J]. Physics Letters B, 778, 207-212(2018).

[39] Xu H J, Zhao J, Wang X B et al. Varying the chiral magnetic effect relative to flow in a single nucleus-nucleus collision[J]. Chinese Physics C, 42, 084103(2018).

[40] Xu H J, Li H L, Wang X B et al. Determine the neutron skin type by relativistic isobaric collisions[J]. Physics Letters B, 819, 136453(2021).

[41] Li P C, Wang Y J, Li Q F et al. Collective flow and nuclear stopping in heavy ion collisions in Fermi energy domain[J]. Nuclear Science and Techniques, 29, 177(2018).

[42] Bhalerao R S, Jaiswal A, Pal S. Collective flow in event-by-event partonic transport plus hydrodynamics hybrid approach[J]. Physical Review C, 92, 014903(2015).

[43] Zheng L, Liu L, Lin Z W et al. Disentangling the development of collective flow in high energy proton proton collisions with a multiphase transport model[J]. The European Physical Journal C, 84, 1029(2024).

[44] Li P C, Wang Y J, Li Q F et al. Accessing the in-medium effects on nucleon-nucleon elastic cross section with collective flows and nuclear stopping[J]. Physics Letters B, 828, 137019(2022).

[45] Keller J, Hebeler K, Schwenk A. Nuclear equation of state for arbitrary proton fraction and temperature based on chiral effective field theory and a Gaussian process emulator[J]. Physical Review Letters, 130, 072701(2023).

[46] Yuan Z Y, Bai D, Wang Z et al. Reliable calculations of nuclear binding energies by the Gaussian process of machine learning[J]. Nuclear Science and Techniques, 35, 105(2024).

[47] Zhou S, Giulani P, Piekarewicz J et al. Reexamining the proton-radius problem using constrained Gaussian processes[J]. Physical Review C, 99, 055202(2019).

[48] Shi S Z, Zhou K, Zhao J X et al. Heavy quark potential in the quark-gluon plasma: Deep neural network meets lattice quantum chromodynamics[J]. Physical Review D, 105, 014017(2022).

[49] Qin F D, Luo H Y, He Z Z et al. Counting of alpha particle tracks on imaging plate based on a convolutional neural network[J]. Nuclear Science and Techniques, 34, 37(2023).

[50] Pang L, Zhou K, Wang X N. Deep learning fornuclear physics[J]. Nuclear Physics Review, 37, 720-726(2020).

[51] Andrieu C, De Freitas N, Doucet A et al. An introduction to MCMC for machine learning[J]. Machine learning, 50, 5-43(2003).

[52] Baldi P, Bauer K, Eng C et al. Jet substructure classification in high-energy physics with deep neural networks[J]. Physical Review D, 93, 094034(2016).

[53] Niu Z M, Fang J Y, Niu Y F. Comparative study of radial basis function and Bayesian neural network approaches in nuclear mass predictions[J]. Physical Review C, 100, 054311(2019).

[54] Carnini M, Pastore A. Trees and forests in nuclear physics[J]. Journal of Physics G: Nuclear and Particle Physics, 47, 082001(2020).

[55] Cai B S, Yuan C X. Random forest-based prediction of decay modes and half-lives of superheavy nuclei[J]. Nuclear Science and Techniques, 34, 204(2023).

[56] Reis I, Baron D, Shahaf S. Probabilistic random forest: a machine learning algorithm for noisy data sets[J]. The Astronomical Journal, 157, 16(2019).

Tools

Get Citation

Copy Citation Text

Guojun WEI, Yongjia WANG, Qingfeng LI, Fuhu LIU. Machine learning simulates collective flow and nuclear stopping in heavy-ion collisions at intermediate energies[J]. NUCLEAR TECHNIQUES, 2025, 48(5): 050005

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites

Paper Information

Category: Special Topics on Applications of Machine Learning in Nuclear Physics and Nuclear Data

Received: Mar. 7, 2025

Accepted: --

Published Online: Jun. 26, 2025

The Author Email: Yongjia WANG (王永佳)

DOI:10.11889/j.0253-3219.2025.hjs.48.250097

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology