Speaker
Jai Bardhan
Description
We present HEP-JEPA, a transformer architecture-based foundation model for tasks at high-energy particle colliders such as the Large Hadron Collider. We pre-train the model on particle jets using a self-supervised strategy inspired by the Joint Embedding Predictive Architecture on the large-scale JetClass dataset containing 100M jets. We evaluate and compare HEP-JEPA to other foundation models on several downstream tasks, such as jet classification and jet observable prediction. HEP-JEPA outperforms or matches the performance of contemporary approaches on these benchmarks.
Authors
Jai Bardhan
Abhiram Tilak
Radhikesh Agrawal
Cyrin Neeraj
Subhadip Mitra