Neural Scaling Laws for Jet Generation

Excerpt

arXiv:2605.28940v2 Announce Type: replace-cross Abstract: 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 particl