Real-time Anomaly Detection at the L1 Trigger of CMS Experiment
This enables incremental testing of new trigger algorithms for high-energy physics experiments without disrupting data collection, addressing the challenge of real-time anomaly detection in particle collisions.
The authors tackled real-time anomaly detection for new physics signatures in the CMS experiment's L1 Trigger during LHC Run 3, achieving prediction within a 50 ns latency at a 40 MHz collision rate using an autoencoder deployed on FPGAs in a test crate.
We present the preparation, deployment, and testing of an autoencoder trained for unbiased detection of new physics signatures in the CMS experiment Global Trigger (GT) test crate FPGAs during LHC Run 3. The GT makes the final decision whether to readout or discard the data from each LHC collision, which occur at a rate of 40 MHz, within a 50 ns latency. The Neural Network makes a prediction for each event within these constraints, which can be used to select anomalous events for further analysis. The GT test crate is a copy of the main GT system, receiving the same input data, but whose output is not used to trigger the readout of CMS, providing a platform for thorough testing of new trigger algorithms on live data, but without interrupting data taking. We describe the methodology to achieve ultra low latency anomaly detection, and present the integration of the DNN into the GT test crate, as well as the monitoring, testing, and validation of the algorithm during proton collisions.