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  • Ngairangbam, Vishal S.; Spannowsky, Michael; Takeuchi, Michihisa (American Physical Society, 2022-05)
    The lack of evidence for new interactions and particles at the Large Hadron Collider (LHC) has motivated the high-energy physics community to explore model-agnostic data-analysis approaches to search for new physics. ...
  • Konar, Partha; Ngairangbam, Vishal S.; Spannowsky, Michael (Springer, 2022-02)
    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 ...
  • Konar, Partha; Ngairangbam, Vishal S. (American Physical Society, 2022-06)
    Vector boson fusion established itself as a highly reliable channel to probe the Higgs boson and an avenue to uncover new physics at the Large Hadron Collider. This channel provides the most stringent bound on Higgs's ...
  • Ngairangbam, Vishal S.; Bhardwaj, Akanksha; Konar, Partha; Nayak, Aruna Kumar (Springer, 2020-11)
    Vector boson fusion proposed initially as an alternative channel for finding heavy Higgs has now established itself as a crucial search scheme to probe different properties of the Higgs boson or for new physics. We explore ...
  • Atkinson, Oliver; Bhardwaj, Akanksha; Englert, Christoph; Konar, Partha; Ngairangbam, Vishal S.; Spannowsky, Michael (Cornell University Library, 2022-04)
    Anomaly detection through employing machine learning techniques has emerged as a novel powerful tool in the search for new physics beyond the Standard Model. Historically similar to the development of jet observables, ...

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