Vertex-Finding and Reconstruction of Contained Two-track Neutrino Events in the MicroBooNE Detector

Abstract

We describe algorithms developed to isolate and accurately reconstruct two-track events that are contained within the MicroBooNE detector. This method is optimized to reconstruct two tracks of lengths longer than 5 cm. This code has applications to searches for neutrino oscillations and measurements of cross sections using quasi-elastic-like charged current events. The algorithms we discuss will be applicable to all detectors running in Fermilab's Short Baseline Neutrino program (SBN), and to any future liquid argon time projection chamber (LArTPC) experiment with beam energies ~1 GeV. The algorithms are publicly available on a GITHUB repository. This reconstruction offers a complementary and independent alternative to the Pandora reconstruction package currently in use in LArTPC experiments, and provides similar reconstruction performance for two-track events.

arXiv comment35 pages, 26 figures

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Cited by in corpus (8)

• Semantic Segmentation with a Sparse Convolutional Neural Network for Event Reconstruction in MicroBooNE
• Search for an anomalous excess of charged-current quasi-elastic ${\nu }_e$ interactions with the MicroBooNE experiment using Deep-Learning-based reconstruction
• A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber
• Scalable, End-to-End, Deep-Learning-Based Data Reconstruction Chain for Particle Imaging Detectors
• Implications of MicroBooNE's low sensitivity to electron antineutrino interactions in the search for the MiniBooNE excess
• A Hybrid 3D/2D Field Response Calculation for Liquid Argon Detectors with PCB Based Anode Plane
• LArTPC hit-based topology classification with quantum machine learning and symmetry
• Sampling Off-Axis Neutrino Fluxes with the Short-Baseline Near Detector