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Time‐lapse electrical resistivity tomography (ERT) data are increasingly used to inform the hydrologic dynamics of mountainous environments at the hillslope scale. Despite their popularity and recent advancements in hydrogeophysical inversion methods, few studies have shown how time‐lapse ERT data can be used to determine hydraulic parameters of subsurface water flow models. This study uses synthetic and field‐collected, hillslope‐scale, time‐lapse ERT data to determine subsurface hydraulic properties of a two‐layer, physics‐based, 2‐D vertical flow model with predefined layer and boundary locations. Uncoupled and coupled hydrogeophysical inversion methods are combined with a fine‐earth fraction optimization scheme to reduce the number of parameters needing calibration and interpret the influence of the hydraulic parameters on the hydrologic model predictions. Inversions of synthetic ERT data recover the prescribed fine‐earth fraction bulk density to within 0.1 g cm⁻³. Field‐collected ERT data from a mountain hillslope result in hydrologic model dynamics that are consistent with previous studies and measured water content data but struggle to capture measured groundwater levels. The uncoupled hydrogeophysical inversion method is more sensitive to changes in hydraulic parameter values of the lower hydrologic model layer than the coupled hydrogeophysical inversion method. Time series of minimum objective function value simulations indicate that periodically collected ERT data may recover hydraulic parameters to a similar level of uncertainty as daily ERT data. Using simple hydrologic model domains within hydrogeophysical inversions shows promise for providing reasonable hydrologic predictions while maintaining relatively simple calibration schemes and should be explored further in future studies.

 

Abstract 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% for 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 $$\pm 0.6$$ ± 0.6 % and 84.1 $$\pm 0.6$$ ± 0.6 %, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation. 

The nature of dark matter and properties of neutrinos are among the most pressing issues in contemporary particle physics. The dual-phase xenon time-projection chamber is the leading technology to cover the available parameter space for weakly interacting massive particles, while featuring extensive sensitivity to many alternative dark matter candidates. These detectors can also study neutrinos through neutrinoless double-beta decay and through a variety of astrophysical sources. A next-generation xenon-based detector will therefore be a true multi-purpose observatory to significantly advance particle physics, nuclear physics, astrophysics, solar physics, and cosmology. This review article presents the science cases for such a detector.

 

Abstract The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype. 

Abstract 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 electromagnetic 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.