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Preprint
Report number arXiv:2411.19506 ; CMS CR-2024/328
Title Real-time Anomaly Detection at the L1 Trigger of CMS Experiment
Author(s) Gandrakota, Abhijith (Fermilab)
Collaboration CMS Collaboration
Document contact Contact: arXiv
Imprint 2024-11-29
Note Contribution to 42nd International Conference on High Energy Physics (ICHEP 2024)
Presented at 42nd International Conference on High Energy Physics (ICHEP 2024), Prague, Czech Republic, 18 - 24 Jul 2024, pp.
Subject category physics.data-an ; Other Fields of Physics ; cs.LG ; Computing and Computers ; hep-ex ; Particle Physics - Experiment
Accelerator/Facility, Experiment CERN LHC ; CMS
Abstract 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.
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Copyright/License preprint: (License: CC BY 4.0)



 


 Record creato 2024-12-03, modificato l'ultima volta il 2024-12-18


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