ML

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
Particle Cloud Generation with Message Passing GANs
There has been significant development recently in generative models for accelerating LHC simulations. Work on simulating jets has …
Apr 10, 2022 New York
Project
Search for boosted Higgs boson pair production in the bbVV all-hadronic final state in CMS
We present developments in a search for boosted (pT > 250 GeV) Higgs boson pair production, where one Higgs decay to bb quarks and …
Apr 9, 2022 New York
Project Slides
Search for boosted Higgs boson pair production in the bbVV all-hadronic final state in CMS
Improving Di-Higgs Sensitivity at Future Colliders in Hadronic Final States with Machine Learning
One of the central goals of the physics program at the future colliders is to elucidate the origin of electroweak symmetry breaking, …
Artur Apresyan, Daniel Diaz, Javier Duarte, Sanmay Ganguly, Raghav Kansal, Nan Lu, Cristina Mantilla Suarez, Samadrita Mukherjee, Cristían Peña, Brian Sheldon, Si Xie
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A FAIR and AI-ready Higgs boson decay dataset
To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of …
Javier Duarte, Haoyang Li, Avik Roy, Ruike Zhu, E. A. Huerta, Daniel Diaz, Philip Harris, Raghav Kansal, Daniel S. Katz, Ishaan H. Kavoori, Volodymyr V. Kindratenko, Farouk Mokhtar, Mark S. Neubauer, Sang Eon Park, Melissa Quinnan, Roger Rusack, Zhizhen Zhao
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Particle Cloud Generation with Message Passing GANs
In high energy physics (HEP), jets are collections of correlated particles produced ubiquitously in particle collisions such as those …
Dec 3, 2021 NeurIPS 21 (Virtual)
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Particle Cloud Generation with Message Passing GANs
Particle Cloud Generation with Message Passing Generative Adversarial Networks
Introduces the message-passing generative adversarial (MPGAN) model and JetNet dataset. We found the physics-informed MPGAN model outperformed all existing point-cloud GANs in simulating high momentum jets.
Raghav Kansal, Javier Duarte, Hao Su, Breno Orzari, Thiago Tomei, Maurizio Pierini, Mary Touranakou, Jean-Roch Vlimant, Dimitrios Gunopulos
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Particle Cloud Generation with Message Passing Generative Adversarial Networks
Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance
Autoencoders have useful applications in high energy physics in anomaly detection, particularly for jets - collimated showers of …
Steven Tsan, Raghav Kansal, Anthony Aportela, Daniel Diaz, Javier Duarte, Sukanya Krishna, Farouk Mokhtar, Jean-Roch Vlimant, Maurizio Pierini
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Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance
Explaining machine-learned particle-flow reconstruction
Developed a graph neural network model to reconstruct particle collisions and interpreted the results using explainable AI techniques.
Farouk Mokhtar, Raghav Kansal, Daniel Diaz, Javier Duarte, Joosep Pata, Maurizio Pierini, Jean-Roch Vlimant
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Explaining machine-learned particle-flow reconstruction