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Automatic trajectory recognition in Active Target Time Projection Chambers data by means of hierarchical clustering
به رسمیت شناختن مسیر خودکار در داده های زمان طرح ریزی زمان فعال هدف با استفاده از خوشه بندی سلسله مراتبی-2019 The automatic reconstruction of three-dimensional particle tracks from Active Target Time Projection
Chambers data can be a challenging task, especially in the presence of noise. In this article, we propose
a non-parametric algorithm that is based on the idea of clustering point triplets instead of the original
points. We define an appropriate distance measure on point triplets and then apply a single-link hierarchical
clustering on the triplets. Compared to parametric approaches like RANSAC or the Hough transform,
the new algorithm has the advantage of potentially finding trajectories even of shapes that are not known
beforehand. This feature is particularly important in low-energy nuclear physics experiments with Active
Targets operating inside a magnetic field. The algorithm has been validated using data from experiments
performed with the Active Target Time Projection Chamber developed at the National Superconducting
Cyclotron Laboratory (NSCL). The results demonstrate the capability of the algorithm to identify and
isolate particle tracks that describe non-analytical trajectories. For curved tracks, the vertex detection
recall was 86% and the precision 94%. For straight tracks, the vertex detection recall was 96% and the
precision 98%. In the case of a test set containing only straight linear tracks, the algorithm performed
better than an iterative Hough transform. Keywords: Time Projection Chambers | Active Target | Pattern recognition | Clustering |
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