Speaker
Description
The integration of foundation models in particle physics is gaining pace rapidly and has expanded the search for new physics. This talk presents foundation models trained on low-level data from the first fully simulated dataset using Open Data Detector (ColliderML), to distinguish between Standard Model and Beyond Standard Model processes. We compare new physics discovery using only low level tracking information with the inclusion of more sources, such as different scales (tracks, particle flow objects) and different detector regions (calorimetry). The effect of these additions in both the supervised and unsupervised case is presented. The talk will further discuss the performance of these approaches in anomaly detection, highlighting the potential of pre-trained foundation models on low-level data for various downstream tasks.