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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2302.00695 |
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| _version_ | 1866916095677431808 |
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| author | Cheng, Taoli Courville, Aaron |
| author_facet | Cheng, Taoli Courville, Aaron |
| contents | As a classical generative modeling approach, energy-based models have the natural advantage of flexibility in the form of the energy function. Recently, energy-based models have achieved great success in modeling high-dimensional data in computer vision and natural language processing. In line with these advancements, we build a multi-purpose energy-based probabilistic model for High Energy Physics events at the Large Hadron Collider. This framework builds on a powerful generative model and describes higher-order inter-particle interactions. It suits different encoding architectures and builds on implicit generation. As for applicative aspects, it can serve as a powerful parameterized event generator for physics simulation, a generic anomalous signal detector free from spurious correlations, and an augmented event classifier for particle identification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_00695 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Versatile Energy-Based Probabilistic Models for High Energy Physics Cheng, Taoli Courville, Aaron Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology As a classical generative modeling approach, energy-based models have the natural advantage of flexibility in the form of the energy function. Recently, energy-based models have achieved great success in modeling high-dimensional data in computer vision and natural language processing. In line with these advancements, we build a multi-purpose energy-based probabilistic model for High Energy Physics events at the Large Hadron Collider. This framework builds on a powerful generative model and describes higher-order inter-particle interactions. It suits different encoding architectures and builds on implicit generation. As for applicative aspects, it can serve as a powerful parameterized event generator for physics simulation, a generic anomalous signal detector free from spurious correlations, and an augmented event classifier for particle identification. |
| title | Versatile Energy-Based Probabilistic Models for High Energy Physics |
| topic | Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2302.00695 |