تقرير
Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
| العنوان: | Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders |
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| المؤلفون: | Touranakou, Mary, Chernyavskaya, Nadezda, Duarte, Javier, Gunopulos, Dimitrios, Kansal, Raghav, Orzari, Breno, Pierini, Maurizio, Tomei, Thiago, Vlimant, Jean-Roch |
| المصدر: | Mach. Learn.: Sci. Technol. 3, 035003 (2022) |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science High Energy Physics - Experiment High Energy Physics - Phenomenology Physics (Other) |
| مصطلحات موضوعية: | Physics - Computational Physics, Computer Science - Machine Learning, High Energy Physics - Experiment, High Energy Physics - Phenomenology |
| الوصف: | 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. Comment: 11 pages, 8 figures |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1088/2632-2153/ac7c56 |
| URL الوصول: | http://arxiv.org/abs/2203.00520 |
| رقم الأكسشن: | edsarx.2203.00520 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1088/2632-2153/ac7c56 |
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