Conditional Deep Generative Models for Simultaneous Simulation and Reconstruction of Entire Events
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910893362642944 |
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| author | Dreyer, Etienne Gross, Eilam Kobylianskii, Dmitrii Mikuni, Vinicius Nachman, Benjamin |
| author_facet | Dreyer, Etienne Gross, Eilam Kobylianskii, Dmitrii Mikuni, Vinicius Nachman, Benjamin |
| contents | We extend the Particle-flow Neural Assisted Simulations (Parnassus) framework of fast simulation and reconstruction to entire collider events. In particular, we use two generative Artificial Intelligence (genAI) tools, continuous normalizing flows and diffusion models, to create a set of reconstructed particle-flow objects conditioned on truth-level particles from CMS Open Simulations. While previous work focused on jets, our updated methods now can accommodate all particle-flow objects in an event along with particle-level attributes like particle type and production vertex coordinates. This approach is fully automated, entirely written in Python, and GPU-compatible. Using a variety of physics processes at the LHC, we show that the extended Parnassus is able to generalize beyond the training dataset and outperforms the standard, public tool Delphes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19981 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Conditional Deep Generative Models for Simultaneous Simulation and Reconstruction of Entire Events Dreyer, Etienne Gross, Eilam Kobylianskii, Dmitrii Mikuni, Vinicius Nachman, Benjamin High Energy Physics - Experiment High Energy Physics - Phenomenology We extend the Particle-flow Neural Assisted Simulations (Parnassus) framework of fast simulation and reconstruction to entire collider events. In particular, we use two generative Artificial Intelligence (genAI) tools, continuous normalizing flows and diffusion models, to create a set of reconstructed particle-flow objects conditioned on truth-level particles from CMS Open Simulations. While previous work focused on jets, our updated methods now can accommodate all particle-flow objects in an event along with particle-level attributes like particle type and production vertex coordinates. This approach is fully automated, entirely written in Python, and GPU-compatible. Using a variety of physics processes at the LHC, we show that the extended Parnassus is able to generalize beyond the training dataset and outperforms the standard, public tool Delphes. |
| title | Conditional Deep Generative Models for Simultaneous Simulation and Reconstruction of Entire Events |
| topic | High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2503.19981 |