PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910338043084800 |
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| author | Leigh, Matthew Sengupta, Debajyoti Quétant, Guillaume Raine, John Andrew Zoch, Knut Golling, Tobias |
| author_facet | Leigh, Matthew Sengupta, Debajyoti Quétant, Guillaume Raine, John Andrew Zoch, Knut Golling, Tobias |
| contents | In this paper, we present a new method to efficiently generate jets in High Energy Physics called PC-JeDi. This method utilises score-based diffusion models in conjunction with transformers which are well suited to the task of generating jets as particle clouds due to their permutation equivariance. PC-JeDi achieves competitive performance with current state-of-the-art methods across several metrics that evaluate the quality of the generated jets. Although slower than other models, due to the large number of forward passes required by diffusion models, it is still substantially faster than traditional detailed simulation. Furthermore, PC-JeDi uses conditional generation to produce jets with a desired mass and transverse momentum for two different particles, top quarks and gluons. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_05376 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics Leigh, Matthew Sengupta, Debajyoti Quétant, Guillaume Raine, John Andrew Zoch, Knut Golling, Tobias High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment In this paper, we present a new method to efficiently generate jets in High Energy Physics called PC-JeDi. This method utilises score-based diffusion models in conjunction with transformers which are well suited to the task of generating jets as particle clouds due to their permutation equivariance. PC-JeDi achieves competitive performance with current state-of-the-art methods across several metrics that evaluate the quality of the generated jets. Although slower than other models, due to the large number of forward passes required by diffusion models, it is still substantially faster than traditional detailed simulation. Furthermore, PC-JeDi uses conditional generation to produce jets with a desired mass and transverse momentum for two different particles, top quarks and gluons. |
| title | PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics |
| topic | High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2303.05376 |