Neural Scaling Laws for Jet Generation
Recently observed empirical scaling laws describe the performance of foundation-type models as three independent key quantities -- dataset size, compute, and model parameters -- are modified. Extracting these scaling laws informs the training of large complex models for which the tuning of hyperparameters in traditional ways is not feasible. This work for the first time explores if scaling laws can also be observed for the task of particle jet generation -- both relevant as a pre-training objective for foundation models and as in-situ simulation by itself. We indeed replicate the key logarithmic scaling law behavior for model-size scaling. Beyond studying the next token prediction validat...
| Authors | Oz Amram, Darius A. Faroughy, Tjarko Gerdes et al. |
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| Primary category | hep-ph |
| Published | 27 May 2026 |
| Last updated at source | 6 October 2026 |
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