Automatizing the search for mass resonances using BumpNet
High Energy Physics - Phenomenology
2025-09-23 v1 High Energy Physics - Experiment
Abstract
Physics Beyond the Standard Model (BSM) has yet to be observed at the Large Hadron Collider (LHC), motivating the development of model-agnostic, machine learning-based strategies to probe more regions of the phase space. As many final states have not yet been examined for mass resonances, an accelerated approach to bump-hunting is desirable. BumpNet is a neural network trained to map smoothly falling invariant-mass histogram data to statistical significance values. It provides a unique, automatized approach to mass resonance searches with the capacity to scan hundreds of final states reliably and efficiently.
Cite
@article{arxiv.2509.16282,
title = {Automatizing the search for mass resonances using BumpNet},
author = {Jean-François Arguin and Georges Azuelos and Émile Baril and Ilan Bessudo and Fannie Bilodeau and Maryna Borysova and Shikma Bressler and Samuel Calvet and Julien Donini and Etienne Dreyer and Michael Kwok Lam Chu and Eva Mayer and Ethan Meszaros and Nilotpal Kakati and Bruna Pascual Dias and Joséphine Potdevin and Amit Shkuri and Muhammad Usman},
journal= {arXiv preprint arXiv:2509.16282},
year = {2025}
}
Comments
Proceedings for EuCAIFCon 2025