ML

Generative transformers and how to evaluate them
Nov 21, 2022 CERN
Project Slides
On the Evaluation of Generative Models in HEP
Nov 1, 2022 Rutgers (Virtual)
Project Slides
JetNet library for machine learning in high energy physics
Sep 16, 2022 Virtual
Project Video Demo
Discussion on Generative Models
Sep 14, 2022 Galileo Galilei Institute, Florence
Project
Particle Cloud Generation with Message Passing GANs
Sep 9, 2022 Galileo Galilei Institute, Florence
Project Slides Video
Overview and Outlook: Machine Learning for Simulation
Jul 21, 2022 CERN
Project Slides
Overview and Outlook: Machine Learning for Simulation
Particle Cloud Generation with Message Passing GANs
Jul 17, 2022 UW Seattle
Project
Machine Learning for LHC Simulations
Jul 14, 2022 Fermilab
Project Slides Video
Machine Learning for LHC Simulations
Particle-based fast jet simulation at the LHC with variational autoencoders
We study how to use deep variational autoencoders (VAEs) for a fast simulation of jets of particles at the Large Hadron Collider. We …
Mary Touranakou, Nadezda Chernyavskaya, Javier Duarte, Dimitrios Gunopulos, Raghav Kansal, Breno Orzari, Maurizio Pierini, Thiago Tomei, Jean-Roch Vlimant
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Particle-based fast jet simulation at the LHC with variational autoencoders
Search for nonresonant pair production of highly energetic Higgs bosons decaying to bottom quarks
Search for events with two high momentum Higgs bosons, using graph neural networks to find Higgs jets. We set the strongest constraints to date on di-Higgs production and the two-vector-boson coupling.
CMS Collaboration
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Search for nonresonant pair production of highly energetic Higgs bosons decaying to bottom quarks