5 citations · 6 across the 5 of their papers we have counts for
6 papers
Automatizing the search for mass resonances using BumpNet
Jean-François Arguin, Georges Azuelos, Émile Baril +15
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 t…
Self-Supervised Learning Strategies for Jet Physics
Patrick Rieck, Kyle Cranmer, Etienne Dreyer +5
We extend the re-simulation-based self-supervised learning approach to learning representations of hadronic jets in colliders by exploiting the Markov property of the standard simu…
Automatizing the search for mass resonances using BumpNet
Jean-Francois Arguin, Georges Azuelos, Émile Baril +15
The search for resonant mass bumps in invariant-mass distributions remains a cornerstone strategy for uncovering Beyond the Standard Model (BSM) physics at the Large Hadron Collide…
Exploring DHCAL design and performance with Graph Neural Networks
M. Borysova, D. Zavazieva, N. Kakati +2
In the context of a gas-sampling Digital Hadronic Calorimeter (DHCAL), we explore the potential of using Graph Neural Networks (GNN) for hadron energy reconstruction and Particle I…
HGPflow: Extending Hypergraph Particle Flow to Collider Event Reconstruction
Nilotpal Kakati, Etienne Dreyer, Anna Ivina +4
In high energy physics, the ability to reconstruct particles based on their detector signatures is essential for downstream data analyses. A particle reconstruction algorithm based…
Advancing Set-Conditional Set Generation: Diffusion Models for Fast Simulation of Reconstructed Particles
Dmitrii Kobylianskii, Nathalie Soybelman, Nilotpal Kakati +3
The computational intensity of detector simulation and event reconstruction poses a significant difficulty for data analysis in collider experiments. This challenge inspires the co…