Speaker
Description
Anomaly detection at the LHC aims to identify events that deviate from dominant Standard Model (SM) processes while minimizing assumptions inherent to predefined trigger selections, enabling model-agnostic searches for new physics. The CMS experiment employs a two-stage trigger system that reduces the LHC bunch-crossing rate of up to 40 MHz to an output rate of approximately 9 kHz for offline processing in Run 3.
This work explores a proposed additional anomaly-detection layer at the High-Level Trigger (HLT), complementing the AXOL1TL system deployed at Level-1. The approach uses self-supervised representation learning to construct a physics-informed latent space in which the main SM processes populate well-separated regions, while anomalous or previously unmodeled event topologies tend to occupy distinct areas.
The model ingests the full set of reconstructed particles and their features, processes them with an attention-based architecture, and produces a compact fixed-size event representation. Preliminary results demonstrate the potential of this strategy to preferentially highlight anomalous events and to achieve rate reduction while improving sensitivity to a broad range of signal scenarios relative to dominant SM backgrounds.