The artificial retina for track reconstruction at the LHC crossing rate

TL;DR

This paper introduces an FPGA-based artificial retina algorithm inspired by mammalian visual processing, capable of real-time, high-quality track reconstruction at the LHC's full crossing rate of 40 MHz.

Contribution

It presents a novel massively parallel pattern-recognition algorithm for particle tracking, demonstrating its feasibility and performance in high-speed FPGA implementations for LHC-scale detectors.

Findings

High-quality tracking achievable with sub-microsecond latency

Algorithm effective for large detectors with complex geometries

Real-time processing at 40 MHz crossing rate demonstrated

Abstract

We present the results of an R&D study for a specialized processor capable of precisely reconstructing events with hundreds of charged-particle tracks in pixel and silicon strip detectors at $40\,\rm MHz$, thus suitable for processing LHC events at the full crossing frequency. For this purpose we design and test a massively parallel pattern-recognition algorithm, inspired to the current understanding of the mechanisms adopted by the primary visual cortex of mammals in the early stages of visual-information processing. The detailed geometry and charged-particle's activity of a large tracking detector are simulated and used to assess the performance of the artificial retina algorithm. We find that high-quality tracking in large detectors is possible with sub-microsecond latencies when the algorithm is implemented in modern, high-speed, high-bandwidth FPGA devices.