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  "authors": [
    {
      "surname": "Wang", 
      "given_names": "Yongjia", 
      "affiliations": [
        {
          "country": "China", 
          "value": "School of Science, Huzhou University, Huzhou, China"
        }
      ], 
      "full_name": "Wang, Yongjia", 
      "orcid": "0000-0003-2506-0010", 
      "email": "wangyongjia@zjhu.edu.cn"
    }, 
    {
      "affiliations": [
        {
          "country": "China", 
          "value": "School of Science, Huzhou University, Huzhou, China"
        }, 
        {
          "country": "China", 
          "value": "School of Science, Zhejiang University of Technology, Hangzhou, China"
        }
      ], 
      "surname": "Li", 
      "given_names": "Fupeng", 
      "full_name": "Li, Fupeng"
    }, 
    {
      "affiliations": [
        {
          "country": "China", 
          "value": "School of Science, Huzhou University, Huzhou, China"
        }, 
        {
          "country": "China", 
          "value": "Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China"
        }
      ], 
      "surname": "Li", 
      "email": "liqf@zjhu.edu.cn", 
      "full_name": "Li, Qingfeng", 
      "given_names": "Qingfeng"
    }, 
    {
      "affiliations": [
        {
          "country": "China", 
          "value": "HiSilicon Research Department, Huawei Technologies Co., Ltd., Shenzhen, China"
        }
      ], 
      "surname": "L\u00fc", 
      "given_names": "Hongliang", 
      "full_name": "L\u00fc, Hongliang"
    }, 
    {
      "orcid": "0000-0001-9859-1758", 
      "affiliations": [
        {
          "country": "Germany", 
          "value": "Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany"
        }
      ], 
      "surname": "Zhou", 
      "given_names": "Kai", 
      "full_name": "Zhou, Kai"
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  "titles": [
    {
      "source": "Elsevier", 
      "title": "Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning"
    }
  ], 
  "collections": [
    {
      "primary": "Physics Letters B"
    }
  ], 
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      "value": "10.1016/j.physletb.2021.136669"
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  "publication_info": [
    {
      "journal_volume": "822 C", 
      "journal_title": "Physics Letters B", 
      "material": "article", 
      "artid": "136669", 
      "year": 2021
    }
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  "$schema": "http://repo.scoap3.org/schemas/hep.json", 
  "abstracts": [
    {
      "source": "Elsevier", 
      "value": "A deep convolutional neural network (CNN) is developed to study symmetry energy (<math><msub><mrow><mi>E</mi></mrow><mrow><mi>sym</mi></mrow></msub><mo>(</mo><mi>\u03c1</mi><mo>)</mo></math>) effects by learning the mapping between the symmetry energy and the two-dimensional (transverse momentum and rapidity) distributions of protons and neutrons in heavy-ion collisions. Supervised training is performed with labeled data-set from the ultrarelativistic quantum molecular dynamics (UrQMD) model simulation. It is found that, by using proton spectra on event-by-event basis as input, the accuracy for classifying the soft and stiff <math><msub><mrow><mi>E</mi></mrow><mrow><mi>sym</mi></mrow></msub><mo>(</mo><mi>\u03c1</mi><mo>)</mo></math> is about 60% due to large event-by-event fluctuations, while by setting event-summed proton spectra as input, the classification accuracy increases to 98%. The accuracies for 5-label (5 different <math><msub><mrow><mi>E</mi></mrow><mrow><mi>sym</mi></mrow></msub><mo>(</mo><mi>\u03c1</mi><mo>)</mo></math>) classification task are about 58% and 72% by using proton and neutron spectra, respectively. For the regression task, the mean absolute errors (MAE) which measure the average magnitude of the absolute differences between the predicted and actual L (the slope parameter of <math><msub><mrow><mi>E</mi></mrow><mrow><mi>sym</mi></mrow></msub><mo>(</mo><mi>\u03c1</mi><mo>)</mo></math>) are about 20.4 and 14.8 MeV by using proton and neutron spectra, respectively. Fingerprints of the density-dependent nuclear symmetry energy on the transverse momentum and rapidity distributions of protons and neutrons can be identified by convolutional neural network algorithm."
    }
  ], 
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      "date": "2021-10-26", 
      "publisher": "Elsevier"
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    "method": "Elsevier", 
    "submission_number": "25232ed0362611ecaa1b7aa32592193b"
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