Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Arguin, Jean-François, Azuelos, Georges, Baril, Émile, Bessudo, Ilan, Bilodeau, Fannie, Borysova, Maryna, Bressler, Shikma, Calvet, Samuel, Donini, Julien, Dreyer, Etienne, Chu, Michael Kwok Lam, Mayer, Eva, Meszaros, Ethan, Kakati, Nilotpal, Dias, Bruna Pascual, Potdevin, Joséphine, Shkuri, Amit, Usman, Muhammad
Institution: Laboratoire de Physique de Clermont Auvergne
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4414744099
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