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<p>The search for resonant mass bumps in invariant-mass distributions remains a cornerstone strategy for uncovering Beyond the Standard Model (BSM) physics at the Large Hadron Collider (LHC). Traditional methods often rely on predefined functional forms and exhaustive computational and human resources, limiting the scope of tested final states and selections. This work presents BumpNet, a machine learning-based approach leveraging advanced neural network architectures to generalize and enhance the Data-Directed Paradigm (DDP) for resonance searches. Trained on a diverse dataset of smoothly-falling analytical functions and realistic simulated data, BumpNet efficiently predicts statistical significance distributions across varying histogram configurations, including those derived from LHC-like conditions. The network’s performance is validated against idealized likelihood ratio-based tests, showing minimal bias and strong sensitivity in detecting mass bumps across a range of scenarios. Additionally, BumpNet’s application to realistic BSM scenarios highlights its capability to identify subtle signals while managing the look-elsewhere effect. These results underscore BumpNet’s potential to expand the reach of resonance searches, paving the way for more comprehensive explorations of LHC data in future analyses.</p>

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Automatizing the search for mass resonances using BumpNet

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月19日

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  • Jean-François Arguin,
  • Georges Azuelos,
  • Émile Baril,
  • Ilan Bessudo,
  • Fannie Bilodeau,
  • Maryna Borysova,
  • Shikma Bressler,
  • Samuel Calvet,
  • Julien Donini,
  • Etienne Dreyer,
  • Michael Kwok Lam Chu,
  • Eva Mayer,
  • Ethan Meszaros,
  • Nilotpal Kakati,
  • Bruna Pascual Dias,
  • Joséphine Potdevin,
  • Amit Shkuri,
  • Eitan Sprejer,
  • Muhammad Usman

摘要

The search for resonant mass bumps in invariant-mass distributions remains a cornerstone strategy for uncovering Beyond the Standard Model (BSM) physics at the Large Hadron Collider (LHC). Traditional methods often rely on predefined functional forms and exhaustive computational and human resources, limiting the scope of tested final states and selections. This work presents BumpNet, a machine learning-based approach leveraging advanced neural network architectures to generalize and enhance the Data-Directed Paradigm (DDP) for resonance searches. Trained on a diverse dataset of smoothly-falling analytical functions and realistic simulated data, BumpNet efficiently predicts statistical significance distributions across varying histogram configurations, including those derived from LHC-like conditions. The network’s performance is validated against idealized likelihood ratio-based tests, showing minimal bias and strong sensitivity in detecting mass bumps across a range of scenarios. Additionally, BumpNet’s application to realistic BSM scenarios highlights its capability to identify subtle signals while managing the look-elsewhere effect. These results underscore BumpNet’s potential to expand the reach of resonance searches, paving the way for more comprehensive explorations of LHC data in future analyses.