Set-conditional set generation for particle physics
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 mod...
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| Veröffentlicht in: | Machine learning: science and technology 2023-12, Vol.4 (4), p.45036 |
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| container_title | Machine learning: science and technology |
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| creator | Soybelman, Nathalie Kakati, Nilotpal Heinrich, Lukas Di Bello, Francesco Armando Dreyer, Etienne Ganguly, Sanmay Gross, Eilam Kado, Marumi Shlomi, Jonathan |
| description | 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. |
| doi_str_mv | 10.1088/2632-2153/ad035b |
| format | Article |
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| subjects | conditional generation fast simulation graph networks Graph neural networks Large Hadron Collider Particle physics slot-attention transformer |
| title | Set-conditional set generation for particle physics |
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