Speaker
Description
We present an overview of current and planned High Energy Physics research activities in A3D3, driven by real-time machine learning. We report the first deployment of ML-based anomaly detection at the Level-1 trigger in both CMS and ATLAS, realized through the AXOL1TL (“Anomaly eXtraction L1 Trigger Lightweight”) and GELATO (“Generic Event-Level Anomalous Trigger Option”) algorithms, respectively. We also highlight advances in the CMS SONIC framework (“Services for Optimized Network Inference on Coprocessors”), enabling inference-as-a-service across heterogeneous computing resources. Finally, we outline ongoing and future developments of next-generation ML triggers and supporting infrastructure for the High-Luminosity LHC, positioning CMS and ATLAS at the forefront of AI-driven discovery.
