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Item type:Publication, First results on ProtoDUNE-SP liquid argon time projection chamber performance from a beam test at the CERN Neutrino Platform(IOP Publishing Ltd, 2020-12-01)The ProtoDUNE-SP detector is a single-phase liquid argon time projection chamber with an active volume of 7.2 × 6.1 × 7.0 m 3 . It is installed at the CERN Neutrino Platform in a specially-constructed beam that delivers charged pions, kaons, protons, muons and electrons with momenta in the range 0.3 GeV/ c to 7 GeV/ c . Beam line instrumentation provides accurate momentum measurements and particle identification. The ProtoDUNE-SP detector is a prototype for the first far detector module of the Deep Underground Neutrino Experiment, and it incorporates full-size components as designed for that module. This paper describes the beam line, the time projection chamber, the photon detectors, the cosmic-ray tagger, the signal processing and particle reconstruction. It presents the first results on ProtoDUNE-SP's performance, including noise and gain measurements, dE / dx calibration for muons, protons, pions and electrons, drift electron lifetime measurements, and photon detector noise, signal sensitivity and time resolution measurements. The measured values meet or exceed the specifications for the DUNE far detector, in several cases by large margins. ProtoDUNE-SP's successful operation starting in 2018 and its production of large samples of high-quality data demonstrate the effectiveness of the single-phase far detector design. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Neutral pion reconstruction using machine learning in the MINERvA experiment at 〈Ev〉 ∼ 6 GeV(IOP Publishing Ltd, 2021-07-01)This paper presents a novel neutral-pion reconstruction that takes advantage of the machine learning technique of semantic segmentation using MINERvA data collected between 2013–2017, with an average neutrino energy of 6 GeV. Semantic segmentation improves the purity of neutral pion reconstruction from two γs from 70.7 ± 0.9% to 89.3 ± 0.7% and improves the efficiency of the reconstruction by approximately 40%. We demonstrate our method in a charged current neutral pion production analysis where a single neutral pion is reconstructed. This technique is applicable to modern tracking calorimeters, such as the new generation of liquid-argon time projection chambers, exposed to neutrino beams with 〈 E ν 〉 between 1–10 GeV. In such experiments it can facilitate the identification of ionization hits which are associated with electromagnetic showers, thereby enabling improved reconstruction of charged-current ν e events arising from ν μ → ν e appearance.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Calibration of the SMAP Soil Moisture Retrieval Algorithm to Reduce Bias over the Amazon Rainforest(European Organization for Nuclear Research, 2023-09-15)Soil moisture (SM) is crucial for the Earth's ecosystem, impacting climate and vegetation health. Obtaining in-situ observations of SM is labor-intensive and complex, particularly in remote and densely vegetated regions like the Amazon rainforest. NASA's Soil Moisture Active and Passive (SMAP) mission, utilizing an L-band radiometer, aims to monitor global SM. While it has been validated in areas with low Vegetation Water Content (VWC) (< 5 kg/m²), its efficiency in the Amazon, which has dense canopies and high VWC (> 10 kg/m²), is uncertain due to scarce in-situ measurements. This study validates and analyzes SMAP data in the Amazon, employing the single-channel algorithm (SCA) and adjusting vegetation optical depth (τ) and single scattering albedo (ω), two key vegetation parameters. It incorporates in-situ SM observations from three old-growth rainforest locations: Tambopata (Southwest Amazon), Manaus (Central Amazon), and Caxiuana (Eastern Amazon). There were substantial discrepancies between SMAP and in-situ data. However, using calibrated τ and ω values, characterized by a lower τ, results in better agreement with in situ measurements. The study emphasizes the pressing need for innovative methodologies to accurately assess SM in high VWC regions like the Amazon using SMAP data. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Bayesian Calibration of a 2d Hydraulic Model Using a Convolutional Neural Network Emulator(RELX Group (Netherlands), 2025-01-01)6