Testing a Neural Network for Anomaly Detection in the CMS Global Trigger Test Crate during Run 3
High Energy Physics - Experiment
2023-12-18 v1 Instrumentation and Detectors
Abstract
We present the deployment and testing of an autoencoder trained for unbiased detection of new physics signatures in the CMS Level-1 Global Trigger (GT) test crate during LHC Run 3. 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 integration of the Neural Network into the GT test crate, and the monitoring, testing, and validation of the algorithm during proton collisions.
Cite
@article{arxiv.2312.10009,
title = {Testing a Neural Network for Anomaly Detection in the CMS Global Trigger Test Crate during Run 3},
author = {Noah Zipper},
journal= {arXiv preprint arXiv:2312.10009},
year = {2023}
}
Comments
6 pages, 5 figures, submitted to JINST for inclusion in TWEPP 2023 conference proceedings