Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
| Autor: | Mary Touranakou, Nadezda Chernyavskaya, Javier Duarte, Dimitrios Gunopulos, Raghav Kansal, Breno Orzari, Maurizio Pierini, Thiago Tomei, Jean-Roch Vlimant |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning hep-ex cs.LG Other Fields of Physics FOS: Physical sciences hep-ph Computational Physics (physics.comp-ph) Computing and Computers Machine Learning (cs.LG) High Energy Physics - Experiment Human-Computer Interaction High Energy Physics - Experiment (hep-ex) High Energy Physics - Phenomenology High Energy Physics - Phenomenology (hep-ph) Artificial Intelligence physics.comp-ph High Energy Physics::Experiment Physics - Computational Physics Software Particle Physics - Experiment Particle Physics - Phenomenology |
| Popis: | 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. Starting from a simulation of the jet before detector effects, we train a Deep Variational Autoencoder to return the corresponding list of constituents after detection. Doing so, we bypass both the time-consuming detector simulation and the collision reconstruction steps of a traditional processing chain, speeding up significantly the events generation workflow. Through model optimization and hyperparameter tuning, we achieve state-of-the-art precision on the jet four-momentum, while providing an accurate description of the constituents momenta, and an inference time comparable to that of a rule-based fast simulation. 11 pages, 8 figures |
| Databáze: | OpenAIRE |
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