Set-Conditional Set Generation for Particle Physics
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arXiv
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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| _version_ | 1866916469287157760 |
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| author | Di Bello, Francesco Armando Dreyer, Etienne Ganguly, Sanmay Gross, Eilam Heinrich, Lukas Kado, Marumi Kakati, Nilotpal Shlomi, Jonathan Soybelman, Nathalie |
| author_facet | Di Bello, Francesco Armando Dreyer, Etienne Ganguly, Sanmay Gross, Eilam Heinrich, Lukas Kado, Marumi Kakati, Nilotpal Shlomi, Jonathan Soybelman, Nathalie |
| contents | The simulation of particle physics data is a fundamental but computationally intensive ingredient for physics analysis at the Large Hadron Collider, where observational set-valued data is generated conditional on a set of incoming particles. To accelerate this task, we present a novel generative model based on a graph neural network and slot-attention components, which exceeds the performance of pre-existing baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_06406 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Set-Conditional Set Generation for Particle Physics Di Bello, Francesco Armando Dreyer, Etienne Ganguly, Sanmay Gross, Eilam Heinrich, Lukas Kado, Marumi Kakati, Nilotpal Shlomi, Jonathan Soybelman, Nathalie High Energy Physics - Experiment The simulation of particle physics data is a fundamental but computationally intensive ingredient for physics analysis at the Large Hadron Collider, where observational set-valued data is generated conditional on a set of incoming particles. To accelerate this task, we present a novel generative model based on a graph neural network and slot-attention components, which exceeds the performance of pre-existing baselines. |
| title | Set-Conditional Set Generation for Particle Physics |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2211.06406 |