Forward Tracking in the ILD Detector

TL;DR

This paper presents a major revision of ILD's reconstruction software, introducing advanced algorithms like Cellular Automaton, Kalman filter, and Hopfield Neural Network to enhance forward track reconstruction.

Contribution

It introduces novel methods and modern standards for track search and fit in ILD's forward region, improving accuracy, speed, and maintainability.

Findings

Enhanced track search efficiency

Improved reconstruction accuracy

Modernized software interface

Abstract

The reconstruction software for ILD is currently subject to a major revision, aiming at improving its accuracy, speed, efficiency and maintainability in time for the upcoming DBD Report. This requires replacing old code by novel methods for track search and fit, together with modern standards for interfaces and tools. Track reconstruction in the "forward region", defined by the silicon Forward Tracking Detector (FTD), relies heavily on a powerful stand-alone track search. The new software makes use of a Cellular Automaton, a Kalman filter, and a Hopfield Neural Network. We give an overview of the project, its methods and merits.