Automatizing the search for mass resonances using BumpNet
hep-ph
/ Authors
Jean-François Arguin, Georges Azuelos, Émile Baril, Ilan Bessudo, Fannie Bilodeau, Maryna Borysova, Shikma Bressler, Samuel Calvet, Julien Donini, Etienne Dreyer
and 8 more authors
Michael Kwok Lam Chu, Eva Mayer, Ethan Meszaros, Nilotpal Kakati, Bruna Pascual Dias, Joséphine Potdevin, Amit Shkuri, Muhammad Usman
/ 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.