Scaling Laws in Jet Classification
Joshua Batson Yonatan Kahn
We demonstrate the emergence of scaling laws in the benchmark top versus QCD
jet classification problem in collider physics. Six distinct
physically-motivated classifiers exhibit power-law scaling of the binary
cross-entropy test loss as a function of training set size, with distinct power
law indices. This result highlights the importance of comparing classifiers as
a function of dataset size rather than for a fixed training set, as the optimal
classifier may change considerably as the dataset is scaled up. We speculate on
the interpretation of our results in terms of previous models of scaling laws
observed in natural language and image datasets.