GENFIT - a Generic Track-Fitting Toolkit

TL;DR

GENFIT is a modular, experiment-independent toolkit for track fitting that has been significantly improved with new algorithms, better visualization, and a revised data model, supporting various detector types.

Contribution

The paper introduces an enhanced version of GENFIT with new algorithms, improved Kalman fitters, and a flexible data model for diverse tracking detectors.

Findings

Enhanced track-fitting algorithms implemented

Improved visualization capabilities added

Revised data model enables efficient track processing

Abstract

GENFIT is an experiment-independent track-fitting toolkit that combines fitting algorithms, track representations, and measurement geometries into a modular framework. We report on a significantly improved version of GENFIT, based on experience gained in the Belle II, PANDA, and FOPI experiments. Improvements concern the implementation of additional track-fitting algorithms, enhanced implementations of Kalman fitters, enhanced visualization capabilities, and additional implementations of measurement types suited for various kinds of tracking detectors. The data model has been revised, allowing for efficient track merging, smoothing, residual calculation, alignment, and storage.