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Abstract:

The Deep Underground Neutrino Experiment (DUNE) is an international, world-class experiment aimed at exploring fundamental questions about the universe that are at the forefront of astrophysics and particle physics research. DUNE will study questions pertaining to the preponderance of matter over antimatter in the early universe, the dynamics of supernovae, the subtleties of neutrino interaction physics, and a number of beyond the Standard Model topics accessible in a powerful neutrino beam. A critical component of the DUNE physics program involves the study of changes in a powerful beam of neutrinos, i.e., neutrino oscillations, as the neutrinos propagate a long distance. The experiment consists of a near detector, sited close to the source of the beam, and a far detector, sited along the beam at a large distance. This document, the DUNE Near Detector Conceptual Design Report (CDR), describes the design of the DUNE near detector and the science program that drives the design and technology choices. The goals and requirements underlying the design, along with projected performance are given. It serves as a starting point for a more detailed design that will be described in future documents.

Abstract:

We investigate the feasibility of using deep learning techniques, in the form of a one-dimensional convolutional neural network (1D-CNN), for the extraction of signals from the raw waveforms produced by the individual channels of liquid argon time projection chamber (LArTPC) detectors. A minimal generic LArTPC detector model is developed to generate realistic noise and signal waveforms used to train and test the 1D-CNN, and evaluate its performance on low-level signals. We demonstrate that our approach overcomes the inherent shortcomings of traditional cut-based methods by extending sensitivity to signals with ADC values below their imposed thresholds. This approach exhibits great promise in enhancing the capabilities of future generation neutrino experiments like DUNE to carry out their low-energy neutrino physics programs

Abstract:

© 2021 authors. Published by the American Physical Society. We report measurements by the T2K experiment of the parameters θ23 and Δm322, which govern the disappearance of muon neutrinos and antineutrinos in the three-flavor PMNS neutrino oscillation model at T2K's neutrino energy and propagation distance. Utilizing the ability of the experiment to run with either a mainly neutrino or a mainly antineutrino beam, muon-like events from each beam mode are used to measure these parameters separately for neutrino and antineutrino oscillations. Data taken from 1.49×1021 protons on target (POT) in neutrino mode and 1.64×1021 POT in antineutrino mode are used. The best-fit values obtained by T2K were sin2(θ23)=0.51-0.07+0.06(0.43-0.05+0.21) and Δm322=2.47-0.09+0.08(2.50-0.13+0.18)×10-3 eV2/c4 for neutrinos (antineutrinos). No significant differences between the values of the parameters describing the disappearance of muon neutrinos and antineutrinos were observed. An analysis using an effective two-flavor neutrino oscillation model where the sine of the mixing angle is allowed to take nonphysical values larger than 1 is also performed to check the consistency of our data with the three-flavor model. Our data were found to be consistent with a physical value for the mixing angle.