Interest Point Detection for Reconstruction in High Granularity Tracking Detectors

TL;DR

This paper explores applying image processing interest point detection algorithms to high granularity tracking detector data, aiming to automate event reconstruction by identifying key physical features with high localization accuracy.

Contribution

It demonstrates the effectiveness of interest point detection in localizing physical features in detector data, reducing reliance on human operators for event reconstruction.

Findings

93% interest points within 5mm of physical features

85% primary vertex and track ends detected in both projections in 85% of events

Delta electrons can be effectively detected

Abstract

This paper presents an investigation of the use of interest point detection algorithms from image processing applied to reconstruction of interactions in high granularity tracking detectors. Their purpose is to extract keypoints from the data as input to higher level reconstruction algorithms, replacing the role of human operators in event selection and reconstruction guidance. Simulations of nu_mu charged current events in a small liquid argon time projection chamber are used as a concrete example of a modern high granularity tracking detector. Data from the simulations are used to characterize the localization of interest points to physical features and the efficiency of finding interest points associated with the primary vertex and track ends is measured. A high degree of localization is found, with 93% of detected interest points found within 5mm of a physical feature. Working in two 2D projections, the primary vertex and both track ends are found in both projections in 85% of events. It is also shown that delta electrons can be detected.