Identifying Merged Tracks in Dense Environments with Machine Learning

TL;DR

This paper presents a machine learning method using boosted decision trees to identify merged tracks in dense environments at the LHC, improving tau decay reconstruction efficiency with minimal mistagging.

Contribution

The paper introduces a novel boosted decision tree technique for classifying merged tracks, enhancing tracking accuracy in high-density particle collision environments.

Findings

Improved tau decay reconstruction efficiency.

Low mistag rate for merged track classification.

Effective recovery of track counts in dense environments.

Abstract

Tracking in high density environments plays an important role in many physics analyses at the LHC. In such environments, it is possible that two nearly collinear particles contribute to the same hits as they travel through the ATLAS pixel detector and semiconductor tracker. If the two particles are sufficiently collinear, it is possible that only a single track candidate will be created, denominated a "merged track", leading to a decrease in tracking efficiency. These proceedings show a possible new technique that uses a boosted decision tree to classify reconstructed tracks as merged. An application of this new method is the recovery of the number of reconstructed tracks in high transverse momentum three-pronged $\tau$ decays, leading to an increased $\tau$ reconstruction efficiency. The observed mistag rate is small.