142 research outputs found
Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora
Get PDFThe 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/
charged pions and protons are correctly reconstructed and identified with
efficiencies of 86.1% and 84.1%, respectively. The efficiencies
measured for test-beam data are shown to be within 5% of those predicted by the
simulation.Comment: 39 pages, 20 figures. Accepted version. Published version available
in Eur. Phys. J. C 83, 618 (2023)
https://doi.org/10.1140/epjc/s10052-023-11733-
Development of methods for the preparation of radiopure 82Se sources for the SuperNEMO neutrinoless double-beta decay experiment
No full textA radiochemical method for producing 82Se sources with an ultra-low level of contamination of natural radionuclides (40K, decay products of 232Th and 238U) has been developed based on cation-exchange chromatographic purification with reverse removal of impurities. It includes chromatographic separation (purification), reduction, conditioning (which includes decantation, centrifugation, washing, grinding, and drying), and 82Se foil production. The conditioning stage, during which highly dispersed elemental selenium is obtained by the reduction of purified selenious acid (H2SeO3) with sulfur dioxide (SO2) represents the crucial step in the preparation of radiopure 82Se samples. The natural selenium (600 g) was first produced in this procedure in order to refine the method. The technique developed was then used to produce 2.5 kg of radiopure enriched selenium (82Se). The produced 82Se samples were wrapped in polyethylene (12 μm thick) and radionuclides present in the sample were analyzed with the BiPo-3 detector. The radiopurity of the plastic materials (chromatographic column material and polypropylene chemical vessels), which were used at all stages, was determined by instrumental neutron activation analysis. The radiopurity of the 82Se foils was checked by measurements with the BiPo-3 spectrometer, which confirmed the high purity of the final product. The measured contamination level for 208Tl was 8-54 μBq/kg, and for 214Bi the detection limit of 600 μBq/kg has been reached
Highly-parallelized simulation of a pixelated LArTPC on a GPU
No full textThe 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 103 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
Precision Measurement of the Boron to Carbon Flux Ratio in Cosmic Rays from 1.9 GV to 2.6 TV with the Alpha Magnetic Spectrometer on the International Space Station
Get PDFKnowledge of the rigidity dependence of the boron to carbon flux ratio (B/C) is important in understanding the propagation of cosmic rays. The precise measurement of the B/C ratio from 1.9 GV to 2.6 TV, based on 2.3 million boron and 8.3 million carbon nuclei collected by AMS during the first 5 years of operation, is presented. The detailed variation with rigidity of the B/C spectral index is reported for the first time. The B/C ratio does not show any significant structures in contrast to many cosmic ray models that require such structures at high rigidities. Remarkably, above 65 GV, the B/C ratio is well described by a single power law R[superscript Δ] with index Δ=-0.333±0.014(fit)±0.005(syst), in good agreement with the Kolmogorov theory of turbulence which predicts Δ=-1/3 asymptotically.National Science Foundation (U.S.) (Grants 1455202 and 1551980)Wyle Research (Firm) (Grant 2014/T72497)United States. National Aeronautics and Space Administration (NASA Earth and Space Science Fellowship Grant HELIO15F-0005
Neutrino interaction vertex reconstruction in DUNE with Pandora deep learning
No full textThe Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours
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