CERN Accelerating science

CMS Note
Report number	arXiv:2210.02489 ; FERMILAB-PUB-22-776-PPD ; CERN-CMS-NOTE-2022-011 ; CERN-CMS-NOTE-2022-011
Title	Charged Particle Tracking in Real Time using Full-Mesh Data Delivery Architecture and Associative Memory Techniques
Author(s)	Ajuha, Sudha (Sao Paulo State U.) ; Shinoda, Ailton Akira (Sao Paulo State U.) ; Ramalho, Lucas Arruda (Sao Paulo State U.) ; Baulieu, Guillaume (IP2I, Lyon) ; Boudoul, Gaelle (IP2I, Lyon) ; Casarsa, Massimo (INFN, Trieste) ; Cascadan, Andre (Sao Paulo State U.) ; Clement, Emyr (Bristol U.) ; de Paiva, Thiago Costa (Sao Paulo State U.) ; Das, Souvik (Florida U.) ; Dutta, Suchandra (Saha Inst.) ; Eusebi, Ricardo (Texas A-M) ; Fedi, Giacomo (INFN, Pisa) ; Ferreira, Vitor Finotti (Sao Paulo State U.) ; Hahn, Kristian (Northwestern U.) ; Hu, Zhen (Fermilab) ; Jindariani, Sergo (Fermilab) ; Konigsberg, Jacobo (Florida U.) ; Liu, Tiehui (Fermilab) ; Low, Jia Fu (Florida U.) ; MacDonald, Emily (Colorado U.) ; Olsen, Jamieson (Fermilab) ; Palla, Fabrizio (INFN, Pisa) ; Pozzobon, Nicola (INFN, Padua) ; Rathjens, Denis (Texas A-M) ; Ristori, Luciano (Fermilab) ; Rossin, Roberto (INFN, Padua) ; Sung, Kevin (Northwestern U.) ; Tran, Nhan (Fermilab) ; Trovato, Marco (Northwestern U.) ; Ulmer, Keith (U. Colorado, Boulder) ; Vaz, Mario (Sao Paulo State U.) ; Viret, Sebastien (IP2I, Lyon) ; Wu, Jin-Yuan (Fermilab) ; Xu, Zijun (Peking U., Beijing) ; Zorzetti, Silvia (Northwestern U. ; CERN)
Publication	2022-12-05
Imprint	15 Nov 2021
Number of pages	33
In:	JINST 17 (2022) P12002
DOI	10.1088/1748-0221/17/12/P12002
Subject category	Detectors and Experimental Techniques
Accelerator/Facility, Experiment	CERN LHC ; CMS
Abstract	We present a flexible and scalable approach to address the challenges of charged particle track reconstruction in real-time event filters (Level-1 triggers) in collider physics experiments. The method described here is based on a full-mesh architecture for data distribution and relies on the Associative Memory approach to implement a pattern recognition algorithm that quickly identifies and organizes hits associated to trajectories of particles originating from particle collisions. We describe a successful implementation of a demonstration system composed of several innovative hardware and algorithmic elements. The implementation of a full-size system relies on the assumption that an Associative Memory device with the sufficient pattern density becomes available in the future, either through a dedicated ASIC or a modern FPGA. We demonstrate excellent performance in terms of track reconstruction efficiency, purity, momentum resolution, and processing time measured with data from a simulated LHC-like tracking detector.
Copyright/License	publication: © 2022-2024 CERN (License: CC-BY-4.0)

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