Tootfinder

TIGER: A Topology-Agnostic, Hierarchical Graph Network for Event Reconstruction
Nathalie Soybelman, Francesco A. Di Bello, Nilotpal Kakati, Eilam Gross
https://arxiv.org/abs/2510.08162

TIGER: A Topology-Agnostic, Hierarchical Graph Network for Event Reconstruction
Event reconstruction at the LHC, the task of assigning observed physics objects to their true origins, is a central challenge for precision measurements and searches. Many existing machine learning approaches address this problem but rely on a single event topology, restricting their applicability to realistic analyses where multiple signal and background processes with different structures are present. To overcome this, we present TIGER, a novel hierarchical graph network that is fundamentally…

Automatizing the search for mass resonances using BumpNet
Jean-Fran\c{c}ois Arguin, Georges Azuelos, \'Emile 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, Jos\'ephine Potdevin, Amit Shkuri, Muhammad Usman

Automatizing the search for mass resonances using BumpNet
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, automat…