UC Berkeley

Foundation models use large datasets to build an effective representation of data that can be deployed on diverse downstream tasks. Previous research developed the mniearn foundation model for jet physics, using unique properties of particle physics, and showed that it could significantly advance discovery potential across collider experiments. This paper introduces a major upgrade, resulting in the mniearned framework. This framework has three new elements: (1) updates to the model architecture and training, (2) using over jets used for training, and (3) providing well-documented software for accessing all datasets and models. We demonstrate mniearned with three representative tasks: top-quark jet tagging with the community elphes-based benchmark dataset, b tagging with ATLAS full simulation, and anomaly detection with CMS experimental data. In each case, mniearned is the state of the art, further expanding the discovery potential of past, current, and future collider experiments.