DeepTreeGANv2: Iterative Pooling of Point Clouds
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913182717575168 |
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| author | Scham, Moritz Alfons Wilhelm Krücker, Dirk Borras, Kerstin |
| author_facet | Scham, Moritz Alfons Wilhelm Krücker, Dirk Borras, Kerstin |
| contents | In High Energy Physics, detailed and time-consuming simulations are used for particle interactions with detectors. To bypass these simulations with a generative model, the generation of large point clouds in a short time is required, while the complex dependencies between the particles must be correctly modelled. Particle showers are inherently tree-based processes, as each particle is produced by the decay or detector interaction of a particle of the previous generation. In this work, we present a significant extension to DeepTreeGAN, featuring a critic, that is able to aggregate such point clouds iteratively in a tree-based manner. We show that this model can reproduce complex distributions, and we evaluate its performance on the public JetNet 150 dataset. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2312_00042 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DeepTreeGANv2: Iterative Pooling of Point Clouds Scham, Moritz Alfons Wilhelm Krücker, Dirk Borras, Kerstin Data Analysis, Statistics and Probability Machine Learning High Energy Physics - Experiment In High Energy Physics, detailed and time-consuming simulations are used for particle interactions with detectors. To bypass these simulations with a generative model, the generation of large point clouds in a short time is required, while the complex dependencies between the particles must be correctly modelled. Particle showers are inherently tree-based processes, as each particle is produced by the decay or detector interaction of a particle of the previous generation. In this work, we present a significant extension to DeepTreeGAN, featuring a critic, that is able to aggregate such point clouds iteratively in a tree-based manner. We show that this model can reproduce complex distributions, and we evaluate its performance on the public JetNet 150 dataset. |
| title | DeepTreeGANv2: Iterative Pooling of Point Clouds |
| topic | Data Analysis, Statistics and Probability Machine Learning High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2312.00042 |