DCTracks: An Open Dataset for Machine Learning-Based Drift Chamber Track Reconstruction

TL;DR

A Monte Carlo dataset of single- and two-track drift chamber events to advance Machine Learning (ML)-based track reconstruction and results for traditional track reconstruction algorithms and a Graph Neural Networks (GNNs) method are reported, facilitating rigorous, reproducible validation for future research.

Abstract

We introduce a Monte Carlo (MC) dataset of single- and two-track drift chamber events to advance Machine Learning (ML)-based track reconstruction. To enable standardized and comparable evaluation, we define track reconstruction specific metrics and report results for traditional track reconstruction algorithms and a Graph Neural Networks (GNNs) method, facilitating rigorous, reproducible validation for future research.

Figures (14)

• Figure 1: BESIII MDC structure (left) and 3D view of the event (right).
• Figure 2: Displays of the simulated events in the x-y plane for a single-track event (left), a conventional two-track event (middle) and a close-by two-track event (right).
• Figure 3: Displays of reconstructed events in the x-y plane for a single-track event (left), a conventional two-track event (middle) and a close-by two-track event (right). A condensation point on the track provides estimates the track parameters. This concept is closely related to the GNN finding method we use.
• Figure 4: Hit efficiency and hit purity for tracks found by both the GNN Finder and the Baseline Finder. Results are shown as functions of $p_{\mathrm{T}}^{\mathrm{MC}}$ (left column) and $\cos\theta^{\mathrm{MC}}$ (right column) for single-track$\pi^+$ events.
• Figure 5: Track efficiency and track charge efficiency for tracks found by both the GNN Finder (orange) and the Baseline Finder (blue) with and without fitting. Results are shown as functions of $p_\mathrm{T}^{\mathrm{MC}}$ (left column) and $\cos\theta^{\mathrm{MC}}$ (right column) for single-track$\pi^+$ events.