{
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  "authors": [
    {
      "raw_name": "Spencer Chang", 
      "affiliations": [
        {
          "country": "USA", 
          "value": "Department of Physics and Institute for Fundamental Science University of Oregon, Eugene, Oregon 97403, USA"
        }
      ], 
      "surname": "Chang", 
      "given_names": "Spencer", 
      "full_name": "Chang, Spencer"
    }, 
    {
      "raw_name": "Ting-Kuo Chen", 
      "affiliations": [
        {
          "country": "Taiwan", 
          "value": "Department of Physics, National Taiwan University, Taipei 10617, Taiwan"
        }
      ], 
      "surname": "Chen", 
      "given_names": "Ting-Kuo", 
      "full_name": "Chen, Ting-Kuo"
    }, 
    {
      "raw_name": "Cheng-Wei Chiang", 
      "affiliations": [
        {
          "country": "Taiwan", 
          "value": "Department of Physics, National Taiwan University, Taipei 10617, Taiwan"
        }, 
        {
          "country": "China", 
          "value": "Physics Division, National Center for Theoretical Sciences, Taipei, Taiwan 10617, Republic of China"
        }
      ], 
      "surname": "Chiang", 
      "given_names": "Cheng-Wei", 
      "full_name": "Chiang, Cheng-Wei"
    }
  ], 
  "titles": [
    {
      "source": "APS", 
      "title": "Distinguishing <math><msup><mi>W</mi><mo>\u2032</mo></msup></math> signals at hadron colliders using neural networks"
    }
  ], 
  "dois": [
    {
      "value": "10.1103/PhysRevD.103.036016"
    }
  ], 
  "publication_info": [
    {
      "journal_volume": "103", 
      "journal_title": "Physical Review D", 
      "material": "article", 
      "journal_issue": "3", 
      "year": 2021
    }
  ], 
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    "source": "APS", 
    "method": "APS", 
    "submission_number": "5f41244cb4bf11ec837fd6d834be26e1"
  }, 
  "page_nr": [
    24
  ], 
  "license": [
    {
      "url": "https://creativecommons.org/licenses/by/4.0/", 
      "license": "CC-BY-4.0"
    }
  ], 
  "copyright": [
    {
      "statement": "Published by the American Physical Society", 
      "year": "2021"
    }
  ], 
  "control_number": "60297", 
  "record_creation_date": "2021-02-24T17:30:03.696270", 
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  "collections": [
    {
      "primary": "HEP"
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    {
      "primary": "Citeable"
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    {
      "primary": "Published"
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  ], 
  "arxiv_eprints": [
    {
      "categories": [
        "hep-ph"
      ], 
      "value": "2007.14586"
    }
  ], 
  "abstracts": [
    {
      "source": "APS", 
      "value": "We investigate a neural-network-based hypothesis test to distinguish different <math><msup><mi>W</mi><mo>\u2032</mo></msup></math> and charged scalar resonances through the <math><mrow><mo>\u2113</mo><mo>+</mo><msub><mrow><mrow><mi>E</mi></mrow></mrow><mrow><mi>T</mi></mrow></msub></mrow></math> channel at hadron colliders. This is traditionally challenging due to a fourfold ambiguity at proton-proton colliders, such as the Large Hadron Collider. Of the neural network approaches we study, we find a multiclass classifier based on a fully connected neural network trained upon two-dimensional histograms made from kinematic variables of the final state <math><mo>\u2113</mo></math> to be the most powerful. Furthermore, by considering the one-jet processes, we demonstrate that one can generalize to multiple two-dimensional histograms to represent different variable pairs. Finally, as a comparison to traditional approaches, we compare our method with Bayesian hypothesis testing and discuss the pros and cons of each approach. The neural network scheme presented in this paper is a powerful tool that can help probe the properties of charged resonances."
    }
  ], 
  "imprints": [
    {
      "date": "2021-02-24", 
      "publisher": "APS"
    }
  ]
}