113 citations · 185 across the 17 of their papers we have counts for
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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…
GLOW: A Unified Particle Flow Transformer
Dmitrii Kobylianskii, Samuel Van Stroud, Kwok Yiu Wong +5
We present GLOW, a transformer-based particle flow model that combines incidence matrix supervision from HGPflow with a MaskFormer architecture. Evaluated on CLIC detector simulati…
Conditional Deep Generative Models for Simultaneous Simulation and Reconstruction of Entire Events
Etienne Dreyer, Eilam Gross, Dmitrii Kobylianskii +2
We extend the Particle-flow Neural Assisted Simulations (Parnassus) framework of fast simulation and reconstruction to entire collider events. In particular, we use two generative…
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…