Design and implementation of neural network based conditions for the CMS Level-1 Global Trigger upgrade for the HL-LHC

• Published in: Journal of instrumentation Vol. 19; no. 3; p. C03019
• Main Authors: Bortolato, G., Cepeda, M., Heikkilä, J., Huber, B., Leutgeb, E., Rabady, D., Sakulin, H.
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.03.2024
• Subjects: Algorithms; Large Hadron Collider; Luminosity; Machine learning; Neural networks; Solenoids; Trigger algorithms; Trigger concepts and systems (hardware and software)
• ISSN: 1748-0221; 1748-0221
• DOI: 10.1088/1748-0221/19/03/C03019

The CMS detector will be upgraded to maintain, or even improve, the physics acceptance under the harsh data taking conditions foreseen during the High-Luminosity LHC operations. In particular, the trigger system (Level-1 and High Level Triggers) will be completely redesigned to utilize detailed information from sub-detectors at the bunch crossing rate: the upgraded Global Trigger will use high-precision trigger objects to provide the Level-1 decision. Besides cut-based algorithms, novel machine-learning-based algorithms will also be included in the Global Trigger to achieve a higher selection efficiency and detect unexpected signals. Implementation of these novel algorithms is presented, focusing on how the neural network models can be optimized to ensure a feasible hardware implementation. The performance and resource usage of the optimized neural network models are discussed in detail.