  Machine learning has become an essential tool in jet physics. Due to their
complex, high-dimensional nature, jets can be explored holistically by neural
networks in ways that are not possible manually. However, innovations in all
areas of jet physics are proceeding in parallel. We show that specially
constructed machine learning models trained for a specific jet classification
task can improve the accuracy, precision, or speed of all other jet physics
tasks. This is demonstrated by training on a particular multiclass
classification task and then using the learned representation for different
classification tasks, for datasets with a different (full) detector simulation,
for jets from a different collision system ($pp$ versus $ep$), for generative
models, for likelihood ratio estimation, and for anomaly detection. Our
OmniLearn approach is thus a foundation model and is made publicly available
for use in any area where state-of-the-art precision is required for analyses
involving jets and their substructure.
