Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866915213412925440 |
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| author | Birk, Joschka Buhmann, Erik Ewen, Cedric Kasieczka, Gregor Shih, David |
| author_facet | Birk, Joschka Buhmann, Erik Ewen, Cedric Kasieczka, Gregor Shih, David |
| contents | We introduce the first generative model trained on the JetClass dataset. Our model generates jets at the constituent level, and it is a permutation-equivariant continuous normalizing flow (CNF) trained with the flow matching technique. It is conditioned on the jet type, so that a single model can be used to generate the ten different jet types of JetClass. For the first time, we also introduce a generative model that goes beyond the kinematic features of jet constituents. The JetClass dataset includes more features, such as particle-ID and track impact parameter, and we demonstrate that our CNF can accurately model all of these additional features as well. Our generative model for JetClass expands on the versatility of existing jet generation techniques, enhancing their potential utility in high-energy physics research, and offering a more comprehensive understanding of the generated jets. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2312_00123 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information Birk, Joschka Buhmann, Erik Ewen, Cedric Kasieczka, Gregor Shih, David High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment Data Analysis, Statistics and Probability We introduce the first generative model trained on the JetClass dataset. Our model generates jets at the constituent level, and it is a permutation-equivariant continuous normalizing flow (CNF) trained with the flow matching technique. It is conditioned on the jet type, so that a single model can be used to generate the ten different jet types of JetClass. For the first time, we also introduce a generative model that goes beyond the kinematic features of jet constituents. The JetClass dataset includes more features, such as particle-ID and track impact parameter, and we demonstrate that our CNF can accurately model all of these additional features as well. Our generative model for JetClass expands on the versatility of existing jet generation techniques, enhancing their potential utility in high-energy physics research, and offering a more comprehensive understanding of the generated jets. |
| title | Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information |
| topic | High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2312.00123 |