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The determination of the direction of a stellar core collapse via its neutrino emission is crucial for the identification of the progenitor for a multimessenger follow-up. A highly effective method of reconstructing supernova directions within the Deep Underground Neutrino Experiment (DUNE) is introduced. The supernova neutrino pointing resolution is studied by simulating and reconstructing electron-neutrino charged-current absorption on ${}_{40}\mathrm{Ar}$ and elastic scattering of neutrinos on electrons. Procedures to reconstruct individual interactions, including a newly developed technique called “brems flipping,” as well as the burst direction from anmore » ensemble of interactions are described. Performance of the burst direction reconstruction is evaluated for supernovae happening at a distance of 10 kpc for a specific supernova burst flux model. The pointing resolution is found to be 3.4 degrees at 68% coverage for a perfect interaction-channel classification and a fiducial mass of 40 kton, and 6.6 degrees for a 10 kton fiducial mass respectively. Assuming a 4% rate of charged-current interactions being misidentified as elastic scattering, DUNE’s burst pointing resolution is found to be 4.3 degrees (8.7 degrees) at 68% coverage.« less

The Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% formore » the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/c charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$$\pm 0.6$$% and 84.1$$\pm 0.6$$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation.« less

The rapid development of general-purpose computing ongraphics processing units (GPGPU) is allowing the implementationof highly-parallelized Monte Carlo simulation chains for particlephysics experiments. This technique is particularly suitable forthe simulation of a pixelated charge readout for time projectionchambers, given the large number of channels that this technologyemploys. Here we present the first implementation of a fullmicrophysical simulator of a liquid argon time projectionchamber (LArTPC) equipped with light readout and pixelated chargereadout, developed for the DUNE Near Detector. The software isimplemented with an end-to-end set of GPU-optimizedalgorithms. The algorithms have been written in Python andtranslated into CUDA kernels using Numba, a just-in-timemore » compilerfor a subset of Python and NumPy instructions. The GPUimplementation achieves a speed up of four orders of magnitudecompared with the equivalent CPU version. The simulation of thecurrent induced on 10^3 pixels takes around 1 ms on the GPU,compared with approximately 10 s on the CPU. The results of thesimulation are compared against data from a pixel-readout LArTPCprototype.« less

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagneticmore » cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation.« less