27 citations · 103 across the 19 of their papers we have counts for
5 papers · 1 filter
Lorentz group equivariant autoencoders
Zichun Hao, Raghav Kansal, Javier Duarte +1
There has been significant work recently in developing machine learning (ML) models in high energy physics (HEP) for tasks such as classification, simulation, and anomaly detection…
FAIR AI Models in High Energy Physics
Javier Duarte, Haoyang Li, Avik Roy +14
The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientifi…
Do graph neural networks learn traditional jet substructure?
Farouk Mokhtar, Raghav Kansal, Javier Duarte
At the CERN LHC, the task of jet tagging, whose goal is to infer the origin of a jet given a set of final-state particles, is dominated by machine learning methods. Graph neural ne…
Improving Di-Higgs Sensitivity at Future Colliders in Hadronic Final States with Machine Learning
Artur Apresyan, Daniel Diaz, Javier Duarte +8
One of the central goals of the physics program at the future colliders is to elucidate the origin of electroweak symmetry breaking, including precision measurements of the Higgs s…
Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
Mary Touranakou, Nadezda Chernyavskaya, Javier Duarte +6
We study how to use Deep Variational Autoencoders for a fast simulation of jets of particles at the LHC. We represent jets as a list of constituents, characterized by their momenta…