TY - JOUR
T1 - Charged particle tracking in real-time using a full-mesh data delivery architecture and associative memory techniques
AU - Ajuha, Sudha
AU - Akira Shinoda, Ailton
AU - Arruda Ramalho, Lucas
AU - Baulieu, Guillaume
AU - Boudoul, Gaelle
AU - Casarsa, Massimo
AU - Cascadan, Andre
AU - Clement, Emyr
AU - Costa de Paiva, Thiago
AU - Das, Souvik
AU - Dutta, Suchandra
AU - Eusebi, Ricardo
AU - Fedi, Giacomo
AU - Finotti Ferreira, Vitor
AU - Hahn, Kristian
AU - Hu, Zhen
AU - Jindariani, Sergo
AU - Konigsberg, Jacobo
AU - Liu, Tiehui
AU - Fu Low, Jia
AU - MacDonald, Emily
AU - Olsen, Jamieson
AU - Palla, Fabrizio
AU - Pozzobon, Nicola
AU - Rathjens, Denis
AU - Ristori, Luciano
AU - Rossin, Roberto
AU - Sung, Kevin
AU - Tran, Nhan
AU - Trovato, Marco
AU - Ulmer, Keith
AU - Vaz, Mario
AU - Viret, Sebastien
AU - Wu, Jin Yuan
AU - Xu, Zijun
AU - Zorzetti, Silvia
N1 - Publisher Copyright:
© 2022 CERN.
PY - 2022/12/1
Y1 - 2022/12/1
N2 - 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.
AB - 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.
KW - Data acquisition concepts
KW - Online farms and online filtering
KW - Trigger algorithms
KW - Trigger concepts and systems (hardware and software)
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U2 - 10.1088/1748-0221/17/12/P12002
DO - 10.1088/1748-0221/17/12/P12002
M3 - Article
AN - SCOPUS:85143909380
SN - 1748-0221
VL - 17
JO - Journal of Instrumentation
JF - Journal of Instrumentation
IS - 12
M1 - P12002
ER -