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  "authors": [
    {
      "affiliations": [
        {
          "country": "India", 
          "value": "Theoretical Physics Division, Physical Research Laboratory, Shree Pannalal Patel Marg, Ahmedabad, Gujarat, 380009, India", 
          "organization": "Theoretical Physics Division, Physical Research Laboratory"
        }
      ], 
      "surname": "Konar", 
      "email": "konar@prl.res.in", 
      "full_name": "Konar, Partha", 
      "given_names": "Partha"
    }, 
    {
      "affiliations": [
        {
          "country": "India", 
          "value": "Theoretical Physics Division, Physical Research Laboratory, Shree Pannalal Patel Marg, Ahmedabad, Gujarat, 380009, India", 
          "organization": "Theoretical Physics Division, Physical Research Laboratory"
        }
      ], 
      "surname": "Ngairangbam", 
      "email": "vishalng@prl.res.in", 
      "full_name": "Ngairangbam, Vishal", 
      "given_names": "Vishal"
    }, 
    {
      "affiliations": [
        {
          "country": "UK", 
          "value": "Institute for Particle Physics Phenomenology, Durham University, Durham, DH1 3LE, U.K.", 
          "organization": "Durham University"
        }, 
        {
          "country": "UK", 
          "value": "Department of Physics, Durham University, Durham, DH1 3LE, U.K.", 
          "organization": "Durham University"
        }
      ], 
      "surname": "Spannowsky", 
      "email": "michael.spannowsky@durham.ac.uk", 
      "full_name": "Spannowsky, Michael", 
      "given_names": "Michael"
    }
  ], 
  "titles": [
    {
      "source": "Springer", 
      "title": "Hypergraphs in LHC phenomenology \u2014 the next frontier of IRC-safe feature extraction"
    }
  ], 
  "dois": [
    {
      "value": "10.1007/JHEP01(2024)113"
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      "page_end": "22", 
      "journal_title": "Journal of High Energy Physics", 
      "material": "article", 
      "journal_volume": "2024", 
      "artid": "JHEP01(2024)113", 
      "year": 2024, 
      "page_start": "1", 
      "journal_issue": "1"
    }
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    "source": "Springer", 
    "method": "Springer", 
    "submission_number": "f33e3f60029a11ef81c58e7301cc4a06"
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  "page_nr": [
    22
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  "license": [
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      "url": "https://creativecommons.org/licenses//by/4.0", 
      "license": "CC-BY-4.0"
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  "copyright": [
    {
      "holder": "The Author(s)", 
      "year": "2024"
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  ], 
  "control_number": "82873", 
  "record_creation_date": "2024-01-20T15:30:24.096823", 
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  "arxiv_eprints": [
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      "categories": [
        "hep-ph"
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      "value": "2309.17351"
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  "abstracts": [
    {
      "source": "Springer", 
      "value": "In this study, we critically evaluate the approximation capabilities of existing infra-red and collinear (IRC) safe feature extraction algorithms, namely Energy Flow Networks (EFNs) and Energy-weighted Message Passing Networks (EMPNs). Our analysis reveals that these algorithms fall short in extracting features from any N-point correlation that isn\u2019t a power of two, based on the complete basis of IRC safe observables, specifically C-correlators. To address this limitation, we introduce the Hypergraph Energy-weighted Message Passing Networks (H-EMPNs), designed to capture any N-point correlation among particles efficiently. Using the case study of top vs. QCD jets, which holds significant information in its 3-point correlations, we demonstrate that H-EMPNs targeting up to N=3 correlations exhibit superior performance compared to EMPNs focusing on up to N=4 correlations within jet constituents."
    }
  ], 
  "imprints": [
    {
      "date": "2024-01-19", 
      "publisher": "Springer"
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