28 research outputs found

    Triplet lifetime in gaseous argon

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    MiniCLEAN is a single-phase liquid argon dark matter experiment. During the initial cooling phase, impurities within the cold gas (<<140 K) were monitored by measuring the scintillation light triplet lifetime, and ultimately a triplet lifetime of 3.480 ±\pm 0.001 (stat.) ±\pm 0.064 (sys.) Ό\mus was obtained, indicating ultra-pure argon. This is the longest argon triplet time constant ever reported. The effect of quenching of separate components of the scintillation light is also investigated

    Constraining the HEP solar neutrino and diffuse supernova neutrino background fluxes with the Sudbury Neutrino Observatory

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    The Sudbury Neutrino Observatory has demonstrated that the apparent deficit in solar neutrinos observed on Earth is due to matter-enhanced flavor transitions, and provided precision measurements of the relevant oscillation parameters. The low backgrounds and large, spectral charged-current nue-d cross section that enabled these measurements also give SNO unique sensitivity to two yet-unobserved neutrino signals of great interest: the hep solar neutrino flux and the diffuse supernova neutrino background (DSNB). This work presents a joint analysis of all three running configurations of the SNO experiment in order to improve constraints on the hep and DSNB nue fluxes. The crucial uncertainties in the energy response and atmospheric neutrino background, as well as the event selection criteria, are reevaluated. Two analysis approaches are taken, a single-bin counting analysis (hep and DSNB) and multidimensional signal extraction fit (hep), using a random sample representing 1/3 of the total SNO data. These searches are the most sensitive to date for these important signals, and will improve further when the full dataset is analyzed. The SNO+ liquid scintillator experiment is a successor to SNO primarily concerned with a search for neutrinoless double-beta decay (0nubetabeta) in 130Te. The modifications to the SNO detector in preparation for SNO+ and an analysis of the 0nubetabeta sensitivity of this upcoming experiment will also be presented in this work. SNO+ will be the first experiment to load Te into liquid scintillator, and is expected to achieve world-class sensitivity in an initial phase commencing in 2017, with significantly improved sensitivity in an upgraded configuration to follow using much higher Te target mass

    Deep Underground Neutrino Experiment (DUNE) Near Detector Conceptual Design Report

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    International audienceThe 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

    DUNE Offline Computing Conceptual Design Report

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    This document describes Offline Software and Computing for the Deep Underground Neutrino Experiment (DUNE) experiment, in particular, the conceptual design of the offline computing needed to accomplish its physics goals. Our emphasis in this document is the development of the computing infrastructure needed to acquire, catalog, reconstruct, simulate and analyze the data from the DUNE experiment and its prototypes. In this effort, we concentrate on developing the tools and systems that facilitate the development and deployment of advanced algorithms. Rather than prescribing particular algorithms, our goal is to provide resources that are flexible and accessible enough to support creative software solutions as HEP computing evolves and to provide computing that achieves the physics goals of the DUNE experiment.This document describes the conceptual design for the Offline Software and Computing for the Deep Underground Neutrino Experiment (DUNE). The goals of the experiment include 1) studying neutrino oscillations using a beam of neutrinos sent from Fermilab in Illinois to the Sanford Underground Research Facility (SURF) in Lead, South Dakota, 2) studying astrophysical neutrino sources and rare processes and 3) understanding the physics of neutrino interactions in matter. We describe the development of the computing infrastructure needed to achieve the physics goals of the experiment by storing, cataloging, reconstructing, simulating, and analyzing ∌\sim 30 PB of data/year from DUNE and its prototypes. Rather than prescribing particular algorithms, our goal is to provide resources that are flexible and accessible enough to support creative software solutions and advanced algorithms as HEP computing evolves. We describe the physics objectives, organization, use cases, and proposed technical solutions

    Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

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    International audienceLiquid 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

    Highly-parallelized simulation of a pixelated LArTPC on a GPU