Charged Particle Tracking in Real-Time Using a Full-Mesh Data Delivery Architecture and Associative Memory Techniques

• Main Authors: Ajuha, Sudha, Shinoda, Ailton Akira, Ramalho, Lucas Arruda, Baulieu, Guillaume, Boudoul, Gaelle, Casarsa, Massimo, Cascadan, Andre, Clement, Emyr, de Paiva, Thiago Costa, Das, Souvik, Dutta, Suchandra, Eusebi, Ricardo, Fedi, Giacomo, Ferreira, Vitor Finotti, Hahn, Kristian, Hu, Zhen, Jindariani, Sergo, Konigsberg, Jacobo, Liu, Tiehui, Low, Jia Fu, MacDonald, Emily, Olsen, Jamieson, Palla, Fabrizio, Pozzobon, Nicola, Rathjens, Denis, Ristori, Luciano, Rossin, Roberto, Sung, Kevin, Tran, Nhan, Trovato, Marco, Ulmer, Keith, Vaz, Mario, Viret, Sebastien, Wu, Jin-Yuan, Xu, Zijun, Zorzetti, Silvia
• Format: Journal Article
• Language: English
• Published: 05-10-2022
• Subjects: Physics - High Energy Physics - Experiment
• DOI: 10.48550/arxiv.2210.02489

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.