Automatizing the search for mass resonances using BumpNet
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arXiv
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| Format: | Preprint |
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2025
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| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16282 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automatizing the search for mass resonances using BumpNet 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 High Energy Physics - Phenomenology High Energy Physics - Experiment 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. |
| title | Automatizing the search for mass resonances using BumpNet |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2509.16282 |