Design and deployment of a fast neural network for measuring the properties of muons originating from displaced vertices in the CMS Endcap Muon Track Finder

2025/09/25 by E. Yigitbasi, Yigitbasi, Efe
Physics and Astronomy · #Astrophysics and Cosmic Phenomena #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Particle Detector Development and Performance #Particle physics theoretical and experimental studies

paper · pdf · doi:10.48550/arxiv.2509.21062

openalex publication_date 2025/09/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We report on the development, implementation, and performance of a fast neural network used to measure the transverse momentum in the CMS Level-1 Endcap Muon Track Finder. The network aims to improve the triggering efficiency of muons produced in the decays of long-lived particles (LLPs). We implemented it in firmware for a Xilinx Virtex-7 FPGA and deployed it during the LHC Run 3 data-taking in 2023. The new displaced muon triggers that use this algorithm broaden the phase space accessible to the CMS experiment for searches that look for evidence of LLPs that decay into muons.

Citations

• Development of the CMS detector for the CERN LHC Run 3
• Fast inference of deep neural networks in FPGAs for particle physics
• Performance of the CMS Level-1 Trigger

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