High-energy nuclear physics meets machine learning

Wan-Bing He

doi:10.1007/s41365-023-01233-z

Your Location：

Home >

Browse articles >

High-energy nuclear physics meets machine learning

INVITED REVIEW | Updated：2023-08-14

- High-energy nuclear physics meets machine learning
- High-energy nuclear physics meets machine learning
- 核技术（英文版） 2023年34卷第6期文章编号：88
- Affiliations：
  
  1.Key Laboratory of Nuclear Physics and Ion‑beam Application (MOE), Institute of Modern Physics, Fudan University, Shanghai 200433, China
  2.Shanghai Research Center for Theoretical Nuclear Physics, NSFC and Fudan University, Shanghai 200438, China
  3.Institute of Particle Physics and Key Laboratory of Quark and Lepton Physics (MOE), Central China Normal University, Wuhan 430079, China
  4.School of Physics and Center for High Energy Physics, Peking University, Beijing 100871, China
  5.Frankfurt Institute for Advanced Studies (FIAS), 60438 Frankfurt am Main, Germany
- Author bio：
  
  †Email: hewanbing@fudan.edu.cn
  ‡Email: mayugang@fudan.edu.cn
  §Email: lgpang@mail.ccnu.edu.cn
  ¶Email: huichaosong@pku.edu.cn
  **Email: zhou@fias.uni-frankfurt.de
- Funds：
  
  National Natural Science Foundation of China(11890714;12147101;12075098;12247107;12075007);Germany BMBF under the ErUM-Data project;Guangdong Major Project of Basic and Applied Basic Research(2020B0301030008)
- DOI：10.1007/s41365-023-01233-z
  中图分类号：
Scan for full text
引用本文
High-energy nuclear physics meets machine learning[J]. 核技术（英文版）, 2023, 34(6):88

Wan-Bing He, Yu-Gang Ma, Long-Gang Pang, et al. High-energy nuclear physics meets machine learning[J]. Nuclear Science and Techniques, 2023, 34(6):88
High-energy nuclear physics meets machine learning[J]. 核技术（英文版）, 2023, 34(6):88 DOI： 10.1007/s41365-023-01233-z.

Wan-Bing He, Yu-Gang Ma, Long-Gang Pang, et al. High-energy nuclear physics meets machine learning[J]. Nuclear Science and Techniques, 2023, 34(6):88 DOI： 10.1007/s41365-023-01233-z.

摘要

Abstract

Although seemingly disparate, high-energy nuclear physics (HENP) and machine learning (ML) have begun to merge in the last few years, yielding interesting results. It is worthy to raise the profile of utilizing this novel mindset from ML in HENP, to help interested readers see the breadth of activities around this intersection. The aim of this mini-review is to inform the community of the current status and present an overview of the application of ML to HENP. From different aspects and using examples, we examine how scientific questions involving HENP can be answered using ML.

关键词

Keywords

Heavy-ion collisionsMachine learningInitial stateBulk propertiesMedium effectsHard probesObservables

references

L. Benato, et al., Shared Data and Algorithms for Deep Learning in Fundamental Physics. Comput. Softw. Big Sci. 6, 9 (2022). doi: 10.1007/s41781-022-00082-6http://doi.org/10.1007/s41781-022-00082-6

M. Favoni, A. Ipp, D.I. Müller, et al., Lattice Gauge Equivariant Convolutional Neural Networks. Phys. Rev. Lett. 128, 032003 (2022). doi: 10.1103/PhysRevLett.128.032003http://doi.org/10.1103/PhysRevLett.128.032003

Z.P. Gao, Y.J. Wang, H.L. Lü, et al., Machine learning the nuclear mass. Nucl. Sci. Tech. 32, 109 (2021). doi: 10.1007/s41365-021-00956-1http://doi.org/10.1007/s41365-021-00956-1

C. Xie, K. Ni, K. Han, et al., Enhanced search sensitivity to the double beta decay of 136xe to excited states with topological signatures. Science China Physics, Mechanics Astronomy 64, 261011 (2021). doi: 10.1007/s11433-020-1693-6http://doi.org/10.1007/s11433-020-1693-6

X.C. Ming, H.F. Zhang, R.R. Xu, et al., Nuclear mass based on the multi-task learning neural network method. Nucl. Sci. Tech. 33, 48 (2022). doi: 10.1007/s41365-022-01031-zhttp://doi.org/10.1007/s41365-022-01031-z

X. Wu, Y. Lu, P. Zhao, Multi-task learning on nuclear masses and separation energies with the kernel ridge regression. Physics Letters B 834, 137394 (2022). doi: 10.1016/j.physletb.2022.137394http://doi.org/10.1016/j.physletb.2022.137394

X.Z. Li, Q.X. Zhang, H.Y. Tan, et al., Fast nuclide identification based on a sequential bayesian method. Nucl. Sci. Tech. 32, 143 (2021).

Z. Gao, Y. Wang, Q. Li, et al., Application of machine learning to study the effects of quadrupole deformation on the nucleus in heavy-ion collisions at intermediate energies. Science China Physics, Mechanics Astronomy 52, 252010-(2022). doi: 10.1360/SSPMA-2021-0308http://doi.org/10.1360/SSPMA-2021-0308

P. Li, J. Bai, Z. Niu, et al., β-decay half-lives studied using neural network method. Science China Physics, Mechanics Astronomy 52, 252006-(2022). doi: 10.1360/SSPMA-2021-0299http://doi.org/10.1360/SSPMA-2021-0299

R. Wang, Y.G. Ma, R. Wada, et al., Nuclear liquid-gas phase transition with machine learning. Phys. Rev. Research 2, 043202 (2020). doi: 10.1103/PhysRevResearch.2.043202http://doi.org/10.1103/PhysRevResearch.2.043202

Y.D. Song, R. Wang, Y.G.M. Ma, et al., Determining temperature in heavy ion collisions with multiplicity distribution. Phys. Lett. B 814, 136084 (2021). doi: 10.1016/j.physletb.2021.136084http://doi.org/10.1016/j.physletb.2021.136084

W.B. He, Q.F. Li, Y.G. Ma, et al., Machine learning in nuclear physics at low and intermediate energies. (2023). arXiv:2301.06396

L. Ng, Å. Bibrzycki, J. Nys, et al., Deep learning exotic hadrons. Phys. Rev. D 105, L091501 (2022). doi: 10.1103/PhysRevD.105.L091501http://doi.org/10.1103/PhysRevD.105.L091501

Z. Zhang, R. Ma, J. Hu, et al., Approach the Gell-Mann-Okubo formula with machine learning. Chin. Phys. Lett. 39, 111201 (2022). doi: 10.1088/0256-307X/39/11/111201http://doi.org/10.1088/0256-307X/39/11/111201

K. Desai, B. Nachman, J. Thaler, Symmetry discovery with deep learning. Phys. Rev. D 105, 096031 (2022). doi: 10.1103/PhysRevD.105.096031http://doi.org/10.1103/PhysRevD.105.096031

G. Goh, Why momentum really works. Distill. (2017). doi: 10.23915/distill.00006http://doi.org/10.23915/distill.00006

D.P. Kingma, J. Ba, Adam: A method for stochastic optimization. (2014) CoRR /1412.6980,.

J. Steinheimer, L. Pang, K. Zhou, et al., A machine learning study to identify spinodal clumping in high energy nuclear collisions. JHEP 12, 122 (2019). doi: 10.1007/JHEP12(2019)122http://doi.org/10.1007/JHEP12(2019)122

M. Omana Kuttan, J. Steinheimer, K. Zhou, et al., Deep Learning Based Impact Parameter Determination for the CBM Experiment. Particles 4, 47-52 (2021). doi: 10.3390/particles4010006http://doi.org/10.3390/particles4010006

Y.G. Huang, L.G. Pang, X. Luo, et al., Probing criticality with deep learning in relativistic heavy-ion collisions. (2021). arXiv:2107.11828

D.P. Kingma, M. Welling, in 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings, Auto-Encoding Variational Bayes. 2014. arXiv:http://arxiv.org/abs/1312.6114v10http://arxiv.org/abs/1312.6114v10

L. Mosser, O. Dubrule, M.J. Blunt, Reconstruction of three-dimensional porous media using generative adversarial neural networks. 96, 043309 (2017). doi: 10.1103/PhysRevE.96.043309http://doi.org/10.1103/PhysRevE.96.043309

K. Mills, I. Tamblyn, Deep neural networks for direct, featureless learning through observation: The case of two-dimensional spin models. 97, 032119 (2018). doi: 10.1103/PhysRevE.97.032119http://doi.org/10.1103/PhysRevE.97.032119

L. de Oliveira, M. Paganini, B. Nachman, Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis. Comput. Softw. Big Sci. 1, 4 (2017). doi: 10.1007/s41781-017-0004-6http://doi.org/10.1007/s41781-017-0004-6

M. Paganini, L. de Oliveira, B. Nachman, Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters. Phys. Rev. Lett. 120, 042003 (2018). doi: 10.1103/PhysRevLett.120.042003http://doi.org/10.1103/PhysRevLett.120.042003

S. Ravanbakhsh, F. Lanusse, R. Mandelbaum, et al., Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. (2017). arXiv:1609.05796

M. Mustafa, D. Bard, W. Bhimji, et al., CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks. Computational Astrophysics and Cosmology 6, 1 (2019). doi: 10.1186/s40668-019-0029-9http://doi.org/10.1186/s40668-019-0029-9

K. Zhou, G. Endrődi, L.G. Pang, et al., Regressive and generative neural networks for scalar field theory. Phys. Rev. D 100, 011501 (2019). doi: 10.1103/PhysRevD.100.011501http://doi.org/10.1103/PhysRevD.100.011501

J.M. Pawlowski, J.M. Urban, Reducing Autocorrelation Times in Lattice Simulations with Generative Adversarial Networks. Mach. Learn. Sci. Tech. 1, 045011 (2020). doi: 10.1088/2632-2153/abae73http://doi.org/10.1088/2632-2153/abae73

M. Germain, K. Gregor, I. Murray, et al., MADE: Masked Autoencoder for Distribution Estimation. arXiv e-prints arXiv:1502.03509 (2015).

A.v.d. Oord, N. Kalchbrenner, O. Vinyals, et al., in Proceedings of the 30th International Conference on Neural Information Processing Systems, Conditional image generation with pixelcnn decoders. NIPS’16, (Curran Associates Inc., Red Hook, NY, USA, 2016), p. 4797-4805

A. Van Den Oord, N. Kalchbrenner, K. Kavukcuoglu, in Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48, Pixel recurrent neural networks. ICML’16, (JMLR.org, 2016), p. 1747-1756

A.v.d. Oord, S. Dieleman, H. Zen, et al., Wavenet: A generative model for raw audio., cite arxiv:1609.03499 (2016) http://arxiv.org/abs/1609.03499http://arxiv.org/abs/1609.03499

L. Wang, Y. Jiang, L. He, et al., Continuous-Mixture Autoregressive Networks Learning the Kosterlitz-Thouless Transition. Chin. Phys. Lett. 39, 120502 (2022). doi: 10.1088/0256-307X/39/12/120502http://doi.org/10.1088/0256-307X/39/12/120502

L. Dinh, D. Krueger, Y. Bengio, NICE: Non-linear Independent Components Estimation. arXiv e-prints arXiv:1410.8516 (2014).

D. Jimenez Rezende, S. Mohamed, Variational Inference with Normalizing Flows. arXiv e-prints arXiv:1505.05770 (2015).

L. Dinh, J. Sohl-Dickstein, S. Bengio, Density estimation using Real NVP. arXiv e-prints arXiv:1605.08803 (2016).

M.S. Albergo, G. Kanwar, P.E. Shanahan, Flow-based generative models for Markov chain Monte Carlo in lattice field theory. Phys. Rev. D 100, 034515 (2019). doi: 10.1103/PhysRevD.100.034515http://doi.org/10.1103/PhysRevD.100.034515

G. Kanwar, M.S. Albergo, D. Boyda, et al., Equivariant flow-based sampling for lattice gauge theory. Phys. Rev. Lett. 125, 121601 (2020). doi: 10.1103/PhysRevLett.125.121601http://doi.org/10.1103/PhysRevLett.125.121601

D. Boyda, G. Kanwar, S. Racanière, et al., Sampling using SU(N) gauge equivariant flows. Phys. Rev. D 103, 074504 (2021). doi: 10.1103/PhysRevD.103.074504http://doi.org/10.1103/PhysRevD.103.074504

S. Chen, O. Savchuk, S. Zheng, et al., Fourier-Flow model generating Feynman paths. (2022). arXiv:2211.03470

J. Shlens, in A Tutorial on Principal Component Analysis (2014) CoRR arXiv: 1404.1100

Y.G. Ma, S. Zhang, Influence of nuclear structure in relativistic heavy-ion collisions. In: Tanihata, I., Toki, H., Kajino, T. (eds) Handbook of Nuclear Physics. Springer, Singapore. 1–30. doi: 10.1007/978-981-15-8818-1_5-1http://doi.org/10.1007/978-981-15-8818-1_5-1

P. Xiang, Y.S. Zhao, X.G. Huang, Determination of the impact parameter in high-energy heavy-ion collisions via deep learning *. Chin. Phys. C 46, 074110 (2022). doi: 10.1088/1674-1137/ac6490http://doi.org/10.1088/1674-1137/ac6490

F. Li, Y. Wang, H. Lü, et al., Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies. J. Phys. G 47, 115104 (2020). doi: 10.1088/1361-6471/abb1f9http://doi.org/10.1088/1361-6471/abb1f9

L. Li, X. Chen, Y. Cui, et al., Fluctuation mechanism and reconstruction of impact parameter distributions with two-observables for intermediate energy heavy ion collisions. (2022). arXiv:2201.12586

R.Q. Charles, H. Su, M. Kaichun, et al., in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Pointnet: Deep learning on point sets for 3d classification and segmentation. 2017, pp. 77–85. doi: 10.1109/CVPR.2017.16http://doi.org/10.1109/CVPR.2017.16

M. Omana Kuttan, J. Steinheimer, K. Zhou, et al., A fast centrality-meter for heavy-ion collisions at the CBM experiment. Phys. Lett. B 811, 135872 (2020). doi: 10.1016/j.physletb.2020.135872http://doi.org/10.1016/j.physletb.2020.135872

L.G. Pang, K. Zhou, X.N. Wang, Interpretable deep learning for nuclear deformation in heavy ion collisions. (2019). arXiv:1906.06429

Y.L. Cheng, S. Shi, Y.G. Ma, et al., How does Bayesian analysis infer the nucleon distributions in isobar collisions? (2023). arXiv:2301.03910

C. Zhang, J. Jia, Evidence of Quadrupole and Octupole Deformations in Zr96+Zr96 and Ru96+Ru96 Collisions at Ultrarelativistic Energies. Phys. Rev. Lett. 128, 022301 (2022). doi: 10.1103/PhysRevLett.128.022301http://doi.org/10.1103/PhysRevLett.128.022301

W.B. He, Y.G. Ma, X.G. Cao, et al., Giant dipole resonance as a fingerprint of clustering configurations in 12c and 16o. Phys. Rev. Lett. 113, 032506 (2014). doi: 10.1103/PhysRevLett.113.032506http://doi.org/10.1103/PhysRevLett.113.032506

C.Z. Shi, Y.G. Ma, α-clustering effect on flows of direct photons in heavy-ion collisions. Nucl. Sci. Tech. 32, 66 (2021). doi: 10.1007/s41365-021-00897-9http://doi.org/10.1007/s41365-021-00897-9

S. Zhang, Y.G. Ma, J.H. Chen, et al., Nuclear cluster structure effect on elliptic and triangular flows in heavy-ion collisions. Phys. Rev. C 95, 064904 (2017). doi: 10.1103/PhysRevC.95.064904http://doi.org/10.1103/PhysRevC.95.064904

J. He, W.B. He, Y.G. Ma, et al., Machine-learning-based identification for initial clustering structure in relativistic heavy-ion collisions. Phys. Rev. C 104, 044902 (2021). doi: 10.1103/PhysRevC.104.044902http://doi.org/10.1103/PhysRevC.104.044902

D. Adhikari, et al., Accurate Determination of the Neutron Skin Thickness of 208Pb through Parity-Violation in Electron Scattering. Phys. Rev. Lett. 126, 172502 (2021). doi: 10.1103/PhysRevLett.126.172502http://doi.org/10.1103/PhysRevLett.126.172502

J. Xu, Bayesian inference of nucleus resonance and neutron skin. (2023). arXiv:2301.07884

J. Xu, W.J. Xie, B.A. Li, Bayesian inference of nuclear symmetry energy from measured and imagined neutron skin thickness in 116,118,120,122,124,130,132Sn, 208Pb, and 48Ca. Phys. Rev. C 102, 044316 (2020). doi: 10.1103/PhysRevC.102.044316http://doi.org/10.1103/PhysRevC.102.044316

M.B. Tsang, et al., Constraints on the symmetry energy and neutron skins from experiments and theory. Phys. Rev. C 86, 015803 (2012). doi: 10.1103/PhysRevC.86.015803http://doi.org/10.1103/PhysRevC.86.015803

S.H. Cheng, J. Wen, L.G. Cao, et al., Neutron skin thickness of 90Zr and symmetry energy constrained by charge exchange spin-dipole excitations. Chin. Phys. C 47, 024102 (2023). doi: 10.1088/1674-1137/aca38ehttp://doi.org/10.1088/1674-1137/aca38e

X.R. Huang, L.W. Chen, Supernova neutrinos as a precise probe of nuclear neutron skin. Phys. Rev. D 106, 123034 (2022). doi: 10.1103/PhysRevD.106.123034http://doi.org/10.1103/PhysRevD.106.123034

E.A. Teixeira, T. Aumann, C.A. Bertulani, et al., Nuclear fragmentation reactions as a probe of neutron skins in nuclei. Eur. Phys. J. A 58, 205 (2022). doi: 10.1140/epja/s10050-022-00849-whttp://doi.org/10.1140/epja/s10050-022-00849-w

D. Androic, et al., Determination of the 27AI Neutron Distribution Radius from a Parity-Violating Electron Scattering Measurement. Phys. Rev. Lett. 128, 132501 (2022). doi: 10.1103/PhysRevLett.128.132501http://doi.org/10.1103/PhysRevLett.128.132501

H.j. Xu, in 20th International Conference on Strangeness in Quark Matter 2022, Probing neutron skin and symmetry energy with relativistic isobar collisions. 2023. arXiv:2301.08303

N. Kozyrev, A. Svetlichnyi, R. Nepeivoda, et al., Peeling away neutron skin in ultracentral collisions of relativistic nuclei. Eur. Phys. J. A 58, 184 (2022). doi: 10.1140/epja/s10050-022-00832-5http://doi.org/10.1140/epja/s10050-022-00832-5

L.M. Liu, C.J. Zhang, J. Zhou, et al., Probing neutron-skin thickness with free spectator neutrons in ultracentral high-energy isobaric collisions. Phys. Lett. B 834, 137441 (2022). doi: 10.1016/j.physletb.2022.137441http://doi.org/10.1016/j.physletb.2022.137441

Y.J. Huang, L.G. Pang, X.N. Wang, Determining the neutron skin types using deep learning and nuclear collisions: An attempt. Science China Physics, Mechanics Astronomy 52, 252011-(2022). doi: 10.1360/SSPMA-2021-0318http://doi.org/10.1360/SSPMA-2021-0318

U.W. Heinz, H. Song, A.K. Chaudhuri, Dissipative hydrodynamics for viscous relativistic fluids. Phys. Rev. C 73, 034904 (2006). doi: 10.1103/PhysRevC.73.034904http://doi.org/10.1103/PhysRevC.73.034904

P. Romatschke, U. Romatschke, Viscosity Information from Relativistic Nuclear Collisions: How Perfect is the Fluid Observed at RHIC? Phys. Rev. Lett. 99, 172301 (2007). doi: 10.1103/PhysRevLett.99.172301http://doi.org/10.1103/PhysRevLett.99.172301

D. Teaney, The Effects of viscosity on spectra, elliptic flow, and HBT radii. Phys. Rev. C 68, 034913 (2003). doi: 10.1103/PhysRevC.68.034913http://doi.org/10.1103/PhysRevC.68.034913

H.X. Zhang, Y.X. Xiao, J.W. Kang, et al., Phenomenological study of the anisotropic quark matter in the two-flavor Nambu-Jona-Lasinio model. Nucl. Sci. Tech. 33, 150 (2022). doi: 10.1007/s41365-022-01129-4http://doi.org/10.1007/s41365-022-01129-4

S.X. Li, D.Q. Fang, Y.G. Ma, et al., Shear viscosity to entropy density ratio in the boltzmann-uehling-uhlenbeck model. (2011). doi: 10.1103/PhysRevC.84.024607http://doi.org/10.1103/PhysRevC.84.024607

C.L. Zhou, Y.G. Ma, D.Q. Fang, et al., Thermodynamic properties and shear viscosity over entropy-density ratio of the nuclear fireball in a quantum-molecular dynamics model. Phys. Rev. C 88, 024604 (2013). doi: 10.1103/PhysRevC.88.024604http://doi.org/10.1103/PhysRevC.88.024604

D.Q. Fang, Y.G. Ma, C.L. Zhou, Shear viscosity of hot nuclear matter by the mean free path method. Phys. Rev. C 89, 047601 (2014). doi: 10.1103/PhysRevC.89.047601http://doi.org/10.1103/PhysRevC.89.047601

X.G. Deng, P. Danielewicz, Y.G. Ma, et al., Impact of fragment formation on shear viscosity in the nuclear liquid-gas phase transition region. Phys. Rev. C 105, 064613 (2022). doi: 10.1103/PhysRevC.105.064613http://doi.org/10.1103/PhysRevC.105.064613

J.E. Bernhard, J.S. Moreland, S.A. Bass, et al., Applying Bayesian parameter estimation to relativistic heavy-ion collisions: simultaneous characterization of the initial state and quark-gluon plasma medium. Phys. Rev. C 94, 024907 (2016). doi: 10.1103/PhysRevC.94.024907http://doi.org/10.1103/PhysRevC.94.024907

J.E. Bernhard, J.S. Moreland, S.A. Bass, Bayesian estimation of the specific shear and bulk viscosity of quark–gluon plasma. Nature Phys. 15, 1113-1117 (2019). doi: 10.1038/s41567-019-0611-8http://doi.org/10.1038/s41567-019-0611-8

Z. Yang, L.W. Chen, Bayesian Inference of the Specific Shear and Bulk Viscosities of the Quark-Gluon Plasma at Crossover from φ and Ω Observables. (2022). arXiv:2207.13534

M. Omana Kuttan, J. Steinheimer, K. Zhou, et al., The QCD EoS of dense nuclear matter from Bayesian analysis of heavy ion collision data. (2022). arXiv:2211.11670

L.G. Pang, K. Zhou, N. Su, et al., An equation-of-state-meter of quantum chromodynamics transition from deep learning. Nature Commun. 9, 210 (2018). doi: 10.1038/s41467-017-02726-3http://doi.org/10.1038/s41467-017-02726-3

L. Pang, Q. Wang, X.N. Wang, Effects of initial flow velocity fluctuation in event-by-event (3+1)D hydrodynamics. Phys. Rev. C 86, 024911 (2012). doi: 10.1103/PhysRevC.86.024911http://doi.org/10.1103/PhysRevC.86.024911

C. Shen, Z. Qiu, H. Song, et al., The iEBE-VISHNU code package for relativistic heavy-ion collisions. Comput. Phys. Commun. 199, 61-85 (2016). doi: 10.1016/j.cpc.2015.08.039http://doi.org/10.1016/j.cpc.2015.08.039

Z. Yang, T. Luo, W. Chen, et al., 3d structure of jet-induced diffusion wake in an expanding quark-gluon plasma. Phys. Rev. Lett. 130, 052301 (2023). doi: 10.1103/PhysRevLett.130.052301http://doi.org/10.1103/PhysRevLett.130.052301

G. Qin, 3d wakes on the femtometer scale by supersonic jets. Nucl. Sci. Tech. 34, 22 (2023). doi: 10.1007/s41365-023-01182-7http://doi.org/10.1007/s41365-023-01182-7

Y.L. Du, K. Zhou, J. Steinheimer, et al., Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning. Eur. Phys. J. C 80, 516 (2020). doi: 10.1140/epjc/s10052-020-8030-7http://doi.org/10.1140/epjc/s10052-020-8030-7

Y.L. Du, K. Zhou, J. Steinheimer, et al., Identifying the nature of the QCD transition in heavy-ion collisions with deep learning. Nucl. Phys. A 1005, 121891 (2021). doi: 10.1016/j.nuclphysa.2020.121891http://doi.org/10.1016/j.nuclphysa.2020.121891

J. Steinheimer, L.G. Pang, K. Zhou, et al., A machine learning study on spinodal clumping in heavy ion collisions. Nucl. Phys. A 1005, 121867 (2021). doi: 10.1016/j.nuclphysa.2020.121867http://doi.org/10.1016/j.nuclphysa.2020.121867

L. Jiang, L. Wang, K. Zhou, Deep learning stochastic processes with QCD phase transition. Phys. Rev. D 103, 116023 (2021). doi: 10.1103/PhysRevD.103.116023http://doi.org/10.1103/PhysRevD.103.116023

M. Omana Kuttan, K. Zhou, J. Steinheimer, et al., An equation-of-state-meter for CBM using PointNet. JHEP 21, 184 (2020). doi: 10.1007/JHEP10(2021)184http://doi.org/10.1007/JHEP10(2021)184

P. Thaprasop, K. Zhou, J. Steinheimer, et al., Unsupervised Outlier Detection in Heavy-Ion Collisions. Phys. Scripta 96, 064003 (2021). doi: 10.1088/1402-4896/abf214http://doi.org/10.1088/1402-4896/abf214

Y. Wang, F. Li, Q. Li, et al., Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning. Phys. Lett. B 822, 136669 (2021). doi: 10.1016/j.physletb.2021.136669http://doi.org/10.1016/j.physletb.2021.136669

D. Mroczek, M. Hjorth-Jensen, J. Noronha-Hostler, et al., Mapping out the thermodynamic stability of a QCD equation of state with a critical point using active learning. (2022). arXiv:2203.13876

H. Huang, B. Xiao, Z. Liu, et al., Applications of deep learning to relativistic hydrodynamics. Phys. Rev. Res. 3, 023256 (2021). doi: 10.1103/PhysRevResearch.3.023256http://doi.org/10.1103/PhysRevResearch.3.023256

H. Huang, B. Xiao, H. Xiong, et al., Applications of deep learning to relativistic hydrodynamics. Nucl. Phys. A 982, 927-930 (2019). doi: 10.1016/j.nuclphysa.2018.11.004http://doi.org/10.1016/j.nuclphysa.2018.11.004

D.A. Teaney, Viscous Hydrodynamics and the Quark Gluon Plasma, (2010), pp. 207–266. doi: 10.1142/9789814293297_0004http://doi.org/10.1142/9789814293297_0004

P. Romatschke, New Developments in Relativistic Viscous Hydrodynamics. Int. J. Mod. Phys. E 19, 1-53 (2010). doi: 10.1142/S0218301310014613http://doi.org/10.1142/S0218301310014613

U. Heinz, R. Snellings, Collective flow and viscosity in relativistic heavy-ion collisions. Ann. Rev. Nucl. Part. Sci. 63, 123-151 (2013). doi: 10.1146/annurev-nucl-102212-170540http://doi.org/10.1146/annurev-nucl-102212-170540

C. Gale, S. Jeon, B. Schenke, Hydrodynamic Modeling of Heavy-Ion Collisions. Int. J. Mod. Phys. A 28, 1340011 (2013). doi: 10.1142/S0217751X13400113http://doi.org/10.1142/S0217751X13400113

H. Song, Hydrodynamic modelling for relativistic heavy-ion collisions at RHIC and LHC. Pramana 84, 703-715 (2015). doi: 10.1007/s12043-015-0971-2http://doi.org/10.1007/s12043-015-0971-2

H. Song, Y. Zhou, K. Gajdosova, Collective flow and hydrodynamics in large and small systems at the LHC. Nucl. Sci. Tech. 28, 99 (2017). doi: 10.1007/s41365-017-0245-4http://doi.org/10.1007/s41365-017-0245-4

P.F. Kolb, U.W. Heinz, Hydrodynamic description of ultrarelativistic heavy ion collisions. 634–714 (2003). arXiv:nucl-th/0305084

H. Song, Causal Viscous Hydrodynamics for Relativistic Heavy Ion Collisions. Other thesis (8 2009). arXiv:0908.3656

H. Song, U.W. Heinz, Causal viscous hydrodynamics in 2+1 dimensions for relativistic heavy-ion collisions. Phys. Rev. C 77, 064901 (2008). doi: 10.1103/PhysRevC.77.064901http://doi.org/10.1103/PhysRevC.77.064901

H. Song, U.W. Heinz, Suppression of elliptic flow in a minimally viscous quark-gluon plasma. Phys. Lett. B 658, 279-283 (2008). doi: 10.1016/j.physletb.2007.11.019http://doi.org/10.1016/j.physletb.2007.11.019

M.L. Miller, K. Reygers, S.J. Sanders, et al., Glauber modeling in high energy nuclear collisions. Ann. Rev. Nucl. Part. Sci. 57, 205-243 (2007). doi: 10.1146/annurev.nucl.57.090506.123020http://doi.org/10.1146/annurev.nucl.57.090506.123020

T. Hirano, Y. Nara, Eccentricity fluctuation effects on elliptic flow in relativistic heavy ion collisions. Phys. Rev. C 79, 064904 (2009). doi: 10.1103/PhysRevC.79.064904http://doi.org/10.1103/PhysRevC.79.064904

H.J. Drescher, Y. Nara, Effects of fluctuations on the initial eccentricity from the Color Glass Condensate in heavy ion collisions. Phys. Rev. C 75, 034905 (2007). doi: 10.1103/PhysRevC.75.034905http://doi.org/10.1103/PhysRevC.75.034905

H.j. Xu, Z. Li, H. Song, High-order flow harmonics of identified hadrons in 2.76A TeV Pb + Pb collisions. Phys. Rev. C 93, 064905 (2016). doi: 10.1103/PhysRevC.93.064905http://doi.org/10.1103/PhysRevC.93.064905

W. Zhao, H.j. Xu, H. Song, Collective flow in 2.76 A TeV and 5.02 A TeV Pb+Pb collisions. Eur. Phys. J. C 77, 645 (2017). doi: 10.1140/epjc/s10052-017-5186-xhttp://doi.org/10.1140/epjc/s10052-017-5186-x

J.S. Moreland, J.E. Bernhard, S.A. Bass, Alternative ansatz to wounded nucleon and binary collision scaling in high-energy nuclear collisions. Phys. Rev. C 92, 011901 (2015). doi: 10.1103/PhysRevC.92.011901http://doi.org/10.1103/PhysRevC.92.011901

H. Yoon, J.H. Sim, M.J. Han, Analytic continuation via domain knowledge free machine learning. 98, 245101 (2018). doi: 10.1103/PhysRevB.98.245101http://doi.org/10.1103/PhysRevB.98.245101

R. Fournier, L. Wang, O.V. Yazyev, et al., Artificial neural network approach to the analytic continuation problem. Phys. Rev. Lett. 124, 056401 (2020). doi: 10.1103/PhysRevLett.124.056401http://doi.org/10.1103/PhysRevLett.124.056401

L. Kades, J.M. Pawlowski, A. Rothkopf, et al., Spectral Reconstruction with Deep Neural Networks. Phys. Rev. D 102, 096001 (2020). doi: 10.1103/PhysRevD.102.096001http://doi.org/10.1103/PhysRevD.102.096001

L. Wang, S. Shi, K. Zhou, Reconstructing spectral functions via automatic differentiation. Phys. Rev. D 106, L051502 (2022). doi: 10.1103/PhysRevD.106.L051502http://doi.org/10.1103/PhysRevD.106.L051502

J. Horak, J.M. Pawlowski, J. Rodríguez-Quintero, et al., Reconstructing QCD spectral functions with Gaussian processes. Phys. Rev. D 105, 036014 (2022). doi: 10.1103/PhysRevD.105.036014http://doi.org/10.1103/PhysRevD.105.036014

J. Horak, J. Papavassiliou, J.M. Pawlowski, et al., Ghost spectral function from the spectral Dyson-Schwinger equation. Phys. Rev. D 104,. doi: 10.1103/PhysRevD.104.074017http://doi.org/10.1103/PhysRevD.104.074017

A.K. Cyrol, J.M. Pawlowski, A. Rothkopf, et al., Reconstructing the gluon. SciPost Phys. 5, 065 (2018). doi: 10.21468/SciPostPhys.5.6.065http://doi.org/10.21468/SciPostPhys.5.6.065

L. Wang, S. Shi, K. Zhou, in 35th Conference on Neural Information Processing Systems, Automatic differentiation approach for reconstructing spectral functions with neural networks. 2021. arXiv:2112.06206

S. Shi, L. Wang, K. Zhou, Rethinking the ill-posedness of the spectral function reconstruction — Why is it fundamentally hard and how Artificial Neural Networks can help. Comput. Phys. Commun. 282, 108547 (2023). doi: 10.1016/j.cpc.2022.108547http://doi.org/10.1016/j.cpc.2022.108547

S. Soma, L. Wang, S. Shi, et al., Neural network reconstruction of the dense matter equation of state from neutron star observables. JCAP 08, 071 (2022). doi: 10.1088/1475-7516/2022/08/071http://doi.org/10.1088/1475-7516/2022/08/071

S. Soma, L. Wang, S. Shi, et al., Reconstructing the neutron star equation of state from observational data via automatic differentiation. (2022). arXiv:2209.08883

X. Gao, A.D. Hanlon, N. Karthik, et al., Continuum-extrapolated NNLO valence PDF of the pion at the physical point. Phys. Rev. D 106, 114510 (2022). doi: 10.1103/PhysRevD.106.114510http://doi.org/10.1103/PhysRevD.106.114510

M. Zhou, F. Gao, J. Chao, et al., Application of radial basis functions neutral networks in spectral functions. Phys. Rev. D 104, 076011 (2021). doi: 10.1103/PhysRevD.104.076011http://doi.org/10.1103/PhysRevD.104.076011

S. Shi, K. Zhou, J. Zhao, et al., Heavy quark potential in the quark-gluon plasma: Deep neural network meets lattice quantum chromodynamics. Phys. Rev. D 105, 014017 (2022). doi: 10.1103/PhysRevD.105.014017http://doi.org/10.1103/PhysRevD.105.014017

R. Larsen, S. Meinel, S. Mukherjee, et al., Excited bottomonia in quark-gluon plasma from lattice QCD. Phys. Lett. B 800, 135119 (2020). doi: 10.1016/j.physletb.2019.135119http://doi.org/10.1016/j.physletb.2019.135119

D. Lafferty, A. Rothkopf, Improved Gauss law model and in-medium heavy quarkonium at finite density and velocity. Phys. Rev. D 101, 056010 (2020). doi: 10.1103/PhysRevD.101.056010http://doi.org/10.1103/PhysRevD.101.056010

D.S. Broomhead, D. Lowe, Radial basis functions, multi-variable functional interpolation and adaptive networks. Tech. rep., Royal Signals and Radar Establishment Malvern (United Kingdom) (1988)

F. Schwenker, H.A. Kestler, G. Palm, Three learning phases for radial-basis-function networks. Neural networks 14, 439-458 (2001).

L. Beheim, A. Zitouni, F. Belloir, et al., New rbf neural network classifier with optimized hidden neurons number. WSEAS Transactions on Systems 467–472 (2004).

J. Wang, G. Liu, A point interpolation meshless method based on radial basis functions. International Journal for Numerical Methods in Engineering 54, 1623-1648 (2002).

J.C. Carr, W.R. Fright, R.K. Beatson, Surface interpolation with radial basis functions for medical imaging. IEEE transactions on medical imaging 16, 96-107 (1997).

W. Chen, X. Han, G. Li, et al., Deep rbfnet: Point cloud feature learning using radial basis functions (2018). arXiv preprint arXiv:1812.04302.

H. Yoon, J.H. Sim, M.J. Han, Analytic continuation via domain knowledge free machine learning. Phys. Rev. B 98, 245101 (2018). doi: 10.1103/PhysRevB.98.245101http://doi.org/10.1103/PhysRevB.98.245101

K. Zhou, N. Xu, Z. Xu, et al., Medium effects on charmonium production at ultrarelativistic energies available at the CERN Large Hadron Collider. Phys. Rev. C 89, 054911 (2014). doi: 10.1103/PhysRevC.89.054911http://doi.org/10.1103/PhysRevC.89.054911

J. Zhao, K. Zhou, S. Chen, et al., Heavy flavors under extreme conditions in high energy nuclear collisions. Prog. Part. Nucl. Phys. 114, 103801 (2020). doi: 10.1016/j.ppnp.2020.103801http://doi.org/10.1016/j.ppnp.2020.103801

M. Laine, O. Philipsen, P. Romatschke, et al., Real-time static potential in hot QCD. JHEP 03, 054 (2007). doi: 10.1088/1126-6708/2007/03/054http://doi.org/10.1088/1126-6708/2007/03/054

A. Beraudo, J.P. Blaizot, C. Ratti, Real and imaginary-time Q anti-Q correlators in a thermal medium. Nucl. Phys. A 806, 312-338 (2008). doi: 10.1016/j.nuclphysa.2008.03.001http://doi.org/10.1016/j.nuclphysa.2008.03.001

N. Brambilla, J. Ghiglieri, A. Vairo, et al., Static quark-antiquark pairs at finite temperature. Phys. Rev. D 78, 014017 (2008). doi: 10.1103/PhysRevD.78.014017http://doi.org/10.1103/PhysRevD.78.014017

N. Brambilla, M.A. Escobedo, J. Ghiglieri, et al., Heavy Quarkonium in a weakly-coupled quark-gluon plasma below the melting temperature. JHEP 09, 038 (2010). doi: 10.1007/JHEP09(2010)038http://doi.org/10.1007/JHEP09(2010)038

B. Chen, K. Zhou, P. Zhuang, Mean Field Effect on J/Ψ Production in Heavy Ion Collisions. Phys. Rev. C 86, 034906 (2012). doi: 10.1103/PhysRevC.86.034906http://doi.org/10.1103/PhysRevC.86.034906

F.P. Li, H.L. Lü, L.G. Pang, et al., Deep-learning quasi-particle masses from QCD equation of state. (2022). arXiv:2211.07994

R. Baier, Y.L. Dokshitzer, A.H. Mueller, et al., Radiative energy loss of high-energy quarks and gluons in a finite volume quark - gluon plasma. Nucl. Phys. B 483, 291-320 (1997). doi: 10.1016/S0550-3213(96)00553-6http://doi.org/10.1016/S0550-3213(96)00553-6

R. Baier, Y.L. Dokshitzer, A.H. Mueller, et al., Radiative energy loss and p(T) broadening of high-energy partons in nuclei. Nucl. Phys. B 484, 265-282 (1997). doi: 10.1016/S0550-3213(96)00581-0http://doi.org/10.1016/S0550-3213(96)00581-0

M. Gyulassy, X.n. Wang, Multiple collisions and induced gluon Bremsstrahlung in QCD. Nucl. Phys. B 420, 583-614 (1994). doi: 10.1016/0550-3213(94)90079-5http://doi.org/10.1016/0550-3213(94)90079-5

X.f. Guo, X.N. Wang, Multiple scattering, parton energy loss and modified fragmentation functions in deeply inelastic e A scattering. Phys. Rev. Lett. 85, 3591-3594 (2000). doi: 10.1103/PhysRevLett.85.3591http://doi.org/10.1103/PhysRevLett.85.3591

U.A. Wiedemann, Gluon radiation off hard quarks in a nuclear environment: Opacity expansion. Nucl. Phys. B 588, 303-344 (2000). doi: 10.1016/S0550-3213(00)00457-0http://doi.org/10.1016/S0550-3213(00)00457-0

Y. Xu, J.E. Bernhard, S.A. Bass, et al., Data-driven analysis for the temperature and momentum dependence of the heavy-quark diffusion coefficient in relativistic heavy-ion collisions. Phys. Rev. C 97, 014907 (2018). doi: 10.1103/PhysRevC.97.014907http://doi.org/10.1103/PhysRevC.97.014907

Y. He, L.G. Pang, X.N. Wang, Bayesian extraction of jet energy loss distributions in heavy-ion collisions. Phys. Rev. Lett. 122, 252302 (2019). doi: 10.1103/PhysRevLett.122.252302http://doi.org/10.1103/PhysRevLett.122.252302

R. Soltz, Bayesian extraction of $\hat{q}$ with multi-stage jet evolution approach. PoS 2018, 048 (2019). doi: 10.22323/1.345.0048http://doi.org/10.22323/1.345.0048

M. Xie, W. Ke, H. Zhang, et al., Information field based global Bayesian inference of the jet transport coefficient. (2022). arXiv:2206.01340

M. Xie, W. Ke, H. Zhang, et al., Global constraint on the jet transport coefficient from single hadron, dihadron and γ-hadron spectra in high-energy heavy-ion collisions. (2022). arXiv:2208.14419

M. Feickert, B. Nachman, A Living Review of Machine Learning for Particle Physics. (2021). arXiv:2102.02770

K.T. Yi-Lun DU, Daniel PABLOS, Applications of deep learning in jet quenching. Science China Physics, Mechanics Astronomy 52, 252017-(2022). doi: 10.1360/SSPMA-2022-0046http://doi.org/10.1360/SSPMA-2022-0046

Y.L. Du, D. Pablos, K. Tywoniuk, Deep learning jet modifications in heavy-ion collisions. JHEP 21, 206 (2020). doi: 10.1007/JHEP03(2021)206http://doi.org/10.1007/JHEP03(2021)206

Y.L. Du, D. Pablos, K. Tywoniuk, Jet Tomography in Heavy-Ion Collisions with Deep Learning. Phys. Rev. Lett. 128, 012301 (2022). doi: 10.1103/PhysRevLett.128.012301http://doi.org/10.1103/PhysRevLett.128.012301

Z. Yang, Y. He, W. Chen, et al., Deep learning assisted jet tomography for the study of Mach cones in QGP. (2022). arXiv:2206.02393

Y. He, T. Luo, X.N. Wang, et al., Linear Boltzmann Transport for Jet Propagation in the Quark-Gluon Plasma: Elastic Processes and Medium Recoil. Phys. Rev. C 91, 054908 (2015). [Erratum: Phys.Rev.C 97, 019902 (2018)]. doi: 10.1103/PhysRevC.91.054908http://doi.org/10.1103/PhysRevC.91.054908

S. Cao, et al., Multistage Monte-Carlo simulation of jet modification in a static medium. Phys. Rev. C 96, 024909 (2017). doi: 10.1103/PhysRevC.96.024909http://doi.org/10.1103/PhysRevC.96.024909

F.L. Liu, W.J. Xing, X.Y. Wu, et al., QLBT: a linear Boltzmann transport model for heavy quarks in a quark-gluon plasma of quasi-particles. Eur. Phys. J. C 82, 350 (2022). doi: 10.1140/epjc/s10052-022-10308-xhttp://doi.org/10.1140/epjc/s10052-022-10308-x

F.G. Gardim, F. Grassi, M. Luzum, et al., Breaking of factorization of two-particle correlations in hydrodynamics. Phys. Rev. C 87, 031901 (2013). doi: 10.1103/PhysRevC.87.031901http://doi.org/10.1103/PhysRevC.87.031901

S.A. Voloshin, A.M. Poskanzer, R. Snellings, Collective phenomena in non-central nuclear collisions. Landolt-Bornstein 23, 293-333 (2010). doi: 10.1007/978-3-642-01539-7_10http://doi.org/10.1007/978-3-642-01539-7_10

R. Snellings, Elliptic Flow: A Brief Review. New J. Phys. 13, 055008 (2011). doi: 10.1088/1367-2630/13/5/055008http://doi.org/10.1088/1367-2630/13/5/055008

J. Jia, Event-shape fluctuations and flow correlations in ultra-relativistic heavy-ion collisions. J. Phys. G 41, 124003 (2014). doi: 10.1088/0954-3899/41/12/124003http://doi.org/10.1088/0954-3899/41/12/124003

Z. Liu, W. Zhao, H. Song, Principal Component Analysis of collective flow in Relativistic Heavy-Ion Collisions. Eur. Phys. J. C 79, 870 (2019). doi: 10.1140/epjc/s10052-019-7379-yhttp://doi.org/10.1140/epjc/s10052-019-7379-y

R.S. Bhalerao, J.Y. Ollitrault, S. Pal, et al., Principal component analysis of event-by-event fluctuations. Phys. Rev. Lett. 114, 152301 (2015). doi: 10.1103/PhysRevLett.114.152301http://doi.org/10.1103/PhysRevLett.114.152301

A. Mazeliauskas, D. Teaney, Subleading harmonic flows in hydrodynamic simulations of heavy ion collisions. Phys. Rev. C 91, 044902 (2015). doi: 10.1103/PhysRevC.91.044902http://doi.org/10.1103/PhysRevC.91.044902

A. Mazeliauskas, D. Teaney, Fluctuations of harmonic and radial flow in heavy ion collisions with principal components. Phys. Rev. C 93, 024913 (2016). doi: 10.1103/PhysRevC.93.024913http://doi.org/10.1103/PhysRevC.93.024913

P. Bozek, Principal component analysis of the nonlinear coupling of harmonic modes in heavy-ion collisions. Phys. Rev. C 97, 034905 (2018). doi: 10.1103/PhysRevC.97.034905http://doi.org/10.1103/PhysRevC.97.034905

A.M. Sirunyan, et al., Principal-component analysis of two-particle azimuthal correlations in PbPb and p Pb collisions at CMS. Phys. Rev. C 96, 064902 (2017). doi: 10.1103/PhysRevC.96.064902http://doi.org/10.1103/PhysRevC.96.064902

Z. Liu, A. Behera, H. Song, et al., Robustness of principal component analysis of harmonic flow in heavy ion collisions. Phys. Rev. C 102, 024911 (2020). doi: 10.1103/PhysRevC.102.024911http://doi.org/10.1103/PhysRevC.102.024911

Y.S. Zhao, L. Wang, K. Zhou, et al., Detecting the chiral magnetic effect via deep learning. Phys. Rev. C 106, L051901 (2022). doi: 10.1103/PhysRevC.106.L051901http://doi.org/10.1103/PhysRevC.106.L051901

K. Lee, J. Mulligan, M. Ploskoń, et al., Machine learning-based jet and event classification at the Electron-Ion Collider with applications to hadron structure and spin physics. J. High Energy Phys. 3, 1-35 (2023).

Z. Hao, R. Kansal, J. Duarte, et al., Lorentz Group Equivariant Autoencoders. (2022). arXiv:2212.07347

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

Probing nucleon effective mass splitting with light particle emission

Effects of sequential decay on collective flows and nuclear stopping power in heavy-ion collisions at intermediate energies

Verification of neutron-induced fission product yields evaluated by a tensor decompsition model in transport-burnup simulations

Machine learning-based analyses for total ionizing dose effects in bipolar junction transistors

Improvement of machine learning-based vertex reconstruction for large liquid scintillator detectors with multiple types of PMTs