FPGA-RICH: A low-latency, high-throughput online partial particle identification system for the NA62 experiment

FPGA-RICH is an FPGA-based online partial particle identification system for the NA62 experiment utilizing Artificial Intelligence (AI) techniques. Integrated between the readout of the Ring Imaging Cherenkov detector (RICH) and the low-level trigger processor (L0TP+), FPGA-RICH implements a fast pipeline to process in real-time the RICH raw hit data stream, producing trigger-primitives containing elaborate physics information, such as the number of charged particles in a physics event, that L0TP+ can use to improve trigger decision selectivity. An AI algorithm provides classification of events by the number of charged particles ( N r ) with efficiency 83% and purity 85% averaged over four N r classes (0, 1, 2, >=3). The full pipeline throughput has been estimated to be above 9.375 MHz using synthetic data, and the system has been integrated in parasitic mode at NA62 to complete validation at the full experiment event rate of 10 MHz.

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Publication Details

Journal
EPJ Web of Conferences
Published
2025-01-01
DOI
https://doi.org/10.1051/epjconf/202533701280
Primary Topic
Particle physics theoretical and experimental studies
Type
article
Field-Weighted Citation Impact
0.00

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article

FPGA-RICH: A low-latency, high-throughput online partial particle identification system for the NA62 experiment

Cristian Rossi, R. Piandani, Andrea Ciardiello, Paolo Cretaro et al.
EPJ Web of Conferences
Particle physics theoretical and experimental studies
article

FPGA-RICH: A low-latency, high-throughput online partial particle identification system for the NA62 experiment

Cristian Rossi, R. Piandani, Andrea Ciardiello, Paolo Cretaro, Francesco Simula, M. Turisini, P. Vicini, Ottorino Frezza, Roberto Ammendola, L. Pontisso, Francesca Lo Cicero, M. Raggi, Carlotta Chiarini, Michele Martinelli, A. Biagioni, A. Lonardo, Pierpaolo Perticaroli
article en

Abstract

FPGA-RICH is an FPGA-based online partial particle identification system for the NA62 experiment utilizing Artificial Intelligence (AI) techniques. Integrated between the readout of the Ring Imaging Cherenkov detector (RICH) and the low-level trigger processor (L0TP+), FPGA-RICH implements a fast pipeline to process in real-time the RICH raw hit data stream, producing trigger-primitives containing elaborate physics information, such as the number of charged particles in a physics event, that L0TP+ can use to improve trigger decision selectivity. An AI algorithm provides classification of events by the number of charged particles ( N r ) with efficiency 83% and purity 85% averaged over four N r classes (0, 1, 2, >=3). The full pipeline throughput has been estimated to be above 9.375 MHz using synthetic data, and the system has been integrated in parasitic mode at NA62 to complete validation at the full experiment event rate of 10 MHz.

EPJ Web of ConferencesVol. 337
Autonomous University of San Luis Potosí (MX), Istituto Nazionale di Fisica Nucleare, Roma Tor Vergata (IT), Istituto Nazionale di Fisica Nucleare, Sezione di Roma I (IT), European Organization for Nuclear Research (CH), Sapienza University of Rome (IT)
Università Campus Bio-Medico di Roma
Openalex Percentile: Top 87%
Particle physics theoretical and experimental studies
DOIOpen Access PDFOpenAlex
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