Estimating the track-reconstruction efficiency in phenomenological proposals of long-lived-particle searches

TL;DR

This paper introduces TrackEff, a Python tool that estimates the efficiency of reconstructing displaced charged-particle tracks in long-lived-particle searches, aiding phenomenological studies without detailed detector simulations.

Contribution

The paper presents TrackEff, a flexible software package that models detector geometry and hit-based track detection efficiency for phenomenological long-lived-particle search proposals.

Findings

TrackEff accurately estimates track reconstruction efficiency.

The tool is adaptable to different detector geometries.

It facilitates phenomenological studies without full GEANT4 simulations.

Abstract

Phenomenological proposals of searches for new, long-lived particles face a challenge when estimating the reconstruction efficiency of displaced charged-particle tracks. The efficiency depends not only on the charged-particle's momentum vector, but also on its production point in relation to the detector boundary and substructure elements. Phenomenological studies generally do not have access to GEANT4-based simulation, which is used for calculating the efficiency in experimental analyses. To address this need, we have developed TrackEff, a python software package for estimating the track-reconstruction efficiency. The default detector geometry within TrackEff is that of the Belle~II drift chamber. However, tunable parameters enable modification of the geometry to correspond to other tracker configurations. TrackEff uses a simplified model of the tracker to determine the number of detector hits associated with each track. The user can then decide whether the track would be detected based on the number of hits, potentially in association with any other information.