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  "authors": [
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      "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"
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    {
      "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"
        }, 
        {
          "country": "India", 
          "value": "Discipline of Physics, Indian Institute of Technology, Palaj, Gandhinagar, Gujarat, 382424, India", 
          "organization": "Indian Institute of Technology"
        }
      ], 
      "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": "Energy-weighted message passing: an infra-red and collinear safe graph neural network algorithm"
    }
  ], 
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    {
      "value": "10.1007/JHEP02(2022)060"
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      "page_end": "30", 
      "journal_title": "Journal of High Energy Physics", 
      "material": "article", 
      "journal_volume": "2022", 
      "artid": "JHEP02(2022)060", 
      "year": 2022, 
      "page_start": "1", 
      "journal_issue": "2"
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    "submission_number": "24804aa0dd5411eca1a1661ec451a4e4"
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  "copyright": [
    {
      "holder": "The Author(s)", 
      "year": "2022"
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  "abstracts": [
    {
      "source": "Springer", 
      "value": "Hadronic signals of new-physics origin at the Large Hadron Collider can remain hidden within the copiously produced hadronic jets. Unveiling such signatures require highly performant deep-learning algorithms. We construct a class of Graph Neural Networks (GNN) in the message-passing formalism that makes the network output infra-red and collinear (IRC) safe, an important criterion satisfied within perturbative QCD calculations. Including IRC safety of the network output as a requirement in the construction of the GNN improves its explainability and robustness against theoretical uncertainties in the data. We generalise Energy Flow Networks (EFN), an IRC safe deep-learning algorithm on a point cloud, defining energy weighted local and global readouts on GNNs. Applying the simplest of such networks to identify top quarks, W bosons and quark/gluon jets, we find that it outperforms state-of-the-art EFNs. Additionally, we obtain a general class of graph construction algorithms that give structurally invariant graphs in the IRC limit, a necessary criterion for the IRC safety of the GNN output."
    }
  ], 
  "imprints": [
    {
      "date": "2022-02-08", 
      "publisher": "Springer"
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