Generative models on phase space
Deep generative models such as diffusion and flow matching are powerful machine learning tools capable of learning and sampling from high-dimensional distributions. They are particularly useful when the training data appears to be concentrated on a submanifold of the data embedding space. For high-energy physics data, consisting of collections of relativistic energy-momentum 4-vectors, this submanifold can enforce extremely strong physically-motivated priors, such as energy and momentum conservation. If these constraints are learned only approximately, rather than exactly, this can inhibit the interpretability and reliability of such generative models. To remedy this deficiency, we introd...
| Authors | Zachary Bogorad, Ibrahim Elsharkawy, Yonatan Kahn et al. |
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| Primary category | hep-ph |
| Published | 2 April 2026 |
| Last updated at source | 28 April 2026 |
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