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

TL;DR

This paper introduces a scalable real-time charged particle tracking method using a full-mesh data architecture and associative memory techniques, demonstrating high efficiency and accuracy in simulated collider data.

Contribution

It presents a novel full-mesh data distribution architecture combined with associative memory for fast pattern recognition in real-time particle tracking.

Findings

High track reconstruction efficiency and purity

Excellent momentum resolution achieved

Fast processing times demonstrated with simulated data

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.