Iterative Retina for high track multiplicity in a barrel-shape tracker and high magnetic field

TL;DR

This paper presents an iterative Retina algorithm implementation on FPGA for high multiplicity track detection in a high magnetic field barrel tracker, demonstrating over 90% efficiency and purity with improved resolution using a Kalman filter.

Contribution

The paper introduces an iterative Retina approach combined with a Kalman filter for enhanced real-time track reconstruction in high-luminosity collider environments.

Findings

Over 90% efficiency and purity in track fitting

Effective integration of Retina with Kalman filter for improved resolution

Feasibility demonstrated with simulated LHC t-tbar events

Abstract

Real-time track tracking in high energy physics experiments at colliders running at high luminosity is very challenging for trigger systems. To perform pattern-recognition and track fitting in online trigger system, the artificial Retina algorithm has been introduced in the field. Retina can be implemented in the state of the art FPGA devices. Our developments use Retina in an iterative way to identify track for barrel-shape tracker embedded in a high magnetic field and with high track multiplicity. As a benchmark we simulate LHC t-tbar events, with a pile-up of 200 and a GEANT-4 based simulation of a 6-layers barrel tracker detector made of silicon modules. With this sample the performance of the hardware design (resource usage, latency) is evaluated. Both efficiency and purity of the Retina fitting are over 90%. Moreover we have also added a Kalman filter after the Retina fit to improve the resolution on the track parameters. Our simulation results show that the Kalman filter can work well together with the Retina algorithm to find track through t-tbar event and provides high resolutions of the reconstructed parameters.