Aspen Open Jets: unlocking LHC data for foundation models in particle physics - Oz Amram; Luca Anzalone; Joschka Birk; Darius A Faroughy; Anna Hallin; Gregor Kasieczka; Michael Krämer; Ian Pang; Humberto Reyes-Gonzalez; David Shih (Machine Learning: Science and Technology, 2025)
Foundation models are deep learning models pre-trained on large amounts of data which are capable of generalizing to multiple datasets and/or downstream tasks. This work demonstrates how data collected by the CMS experiment at the Large Hadron Collider can be useful in pre-training foundation models for HEP. Specifically, we introduce the AspenOpenJets (AOJs) dataset, consisting of approximately 178 M high $p_T$ jets derived from CMS 2016 Open Data. We show how pre-training the OmniJet - α foundation model on AOJs improves performance on generative tasks with significant domain shift: generating boosted top and QCD jets from the simulated JetClass dataset. In addition to demonstrating the power of pre-training of a jet-based foundation model on actual proton–proton collision data, we provide the ML-ready derived AOJs dataset for further public use.