Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Neutrino detector”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Measurement of neutron-proton capture in the $\mathrmSNO+ water phase

The SNO+ experiment collected data as a low-threshold water Cherenkov detector from September 2017 to July 2019. Measurements of the 2.2-MeV γ 's produced by neutron capture on hydrogen were made using an Am-Be calibration source, for which a large fraction of emitted neutrons are produced simultaneously with a 4.4-MeV γ. Analysis of the delayed coincidence between the 4.4-MeV γ and the 2.2-MeV capture γ revealed a neutron detection efficiency that is centered around 50% and varies at the level of 1% across the inner region of the detector, which to our knowledge is the highest efficiency achieved among pure water Cherenkov detectors. Finally, in addition, the neutron capture time constant was measured and converted to a thermal neutron-proton capture cross section of $336.3^{+1.2}_{-1.5}$ mb .

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

First measurement of electron neutrino scattering cross section on argon

We report the first electron neutrino cross section measurements on argon, based on data collected by the ArgoNeuT experiment running in the GeV-scale NuMI beamline at Fermilab. A flux-averaged $\nu_e + \overline{\nu}_e$ total and a lepton angle differential cross section are extracted using 13 $\nu_e$ and $\overline{\nu}_e$ events identified with fully-automated selection and reconstruction. We employ electromagnetic-induced shower characterization and analysis tools developed to identify $\nu_e/\overline{\nu}_e$-like events among complex interaction topologies present in ArgoNeuT data ($\langle E_{\bar{\nu}_e} \rangle = 4.3$ GeV and $\langle E_{\nu_e} \rangle = 10.5$ GeV). The techniques are widely applicable to searches for electron-flavor appearance at short- and long-baseline using liquid argon time projection chamber technology. Notably, the data-driven studies of GeV-scale $\nu_e/\overline{\nu}_e$ interactions presented in this Letter probe an energy regime relevant for future DUNE oscillation physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Benefits of MeV-scale reconstruction capabilities in large liquid argon time projection chambers

Using truth-level Monte Carlo simulations of particle interactions in a large volume of liquid argon, we demonstrate physics capabilities enabled by reconstruction of topologically compact and isolated low-energy features, or “blips,” in large liquid argon time projection chamber (LArTPC) events. These features are mostly produced by electron products of photon interactions depositing ionization energy. The blip identification capability of the LArTPC is enabled by its unique combination of size, position resolution precision, and low energy thresholds. Here, we show that consideration of reconstructed blips in LArTPC physics analyses can result in substantial improvements in calorimetry for neutrino and new physics interactions and for final-state particles ranging in energy from the MeV to the GeV scale. Blip activity analysis is also shown to enable discrimination between interaction channels and final-state particle types. In addition to demonstrating these gains in calorimetry and discrimination, some limitations of blip reconstruction capabilities and physics outcomes are also discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE

We present the performance of a semantic segmentation network, SparseSSNet, that provides pixel-level classification of MicroBooNE data. The MicroBooNE experiment employs a liquid argon time projection chamber for the study of neutrino properties and interactions. SparseSSNet is a submanifold sparse convolutional neural network, which provides the initial machine learning based algorithm utilized in one of MicroBooNE's ν e -appearance oscillation analyses. The network is trained to categorize pixels into five classes, which are re-classified into two classes more relevant to the current analysis. The output of SparseSSNet is a key input in further analysis steps. This technique, used for the first time in liquid argon time projection chambers data and is an improvement compared to a previously used convolutional neural network, both in accuracy and computing resource utilization. Here, the accuracy achieved on the test sample is ≥ 99%. For full neutrino interaction simulations, the time for processing one image is ≈ 0.5 sec, the memory usage is at 1 GB level, which allows utilization of most typical CPU worker machine.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Kiloton-scale xenon detectors for neutrinoless double beta decay and other new physics searches

We report that large detectors employing xenon are a leading technology in existing and planned searches for new physics, including searches for neutrinoless double beta decay (0νββ) and dark matter. While upcoming detectors will employ target masses of a ton or more, further extending gas- or liquid-phase Xe detectors to the kton scale would enable extremely sensitive next-generation searches for rare phenomena. The key challenge to extending this technology to detectors well beyond the ton scale is the acquisition of the Xe itself. We describe the motivation for extending Xe time-projection chambers to the kton scale and possible avenues for Xe acquisition that avoid existing supply chains. If acquisition of Xe in the required quantities is successful, kton-scale detectors of this type could enable a new generation of experiments, including searches for 0νββ at half-life sensitivities as long as 10 30 yr.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Comparison of simulated neutrino emission models with data on Supernova 1987A

Here we compare models of supernova (SN) neutrino emission with the Kamiokande II data on SN 1987A using the Bayesian approach. These models are taken from simulations and are representative of current one-dimensional SN models. We find that models with a brief accretion phase of neutrino emission are the most favored. This result is not affected by varying the overall flux normalization or considering neutrino oscillations. We also check the compatibility of the best-fit models with the data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Scintillation yield from electronic and nuclear recoils in superfluid He 4

Superfluid $^4$He is a promising target material for direct detection of light ($<$ 1 GeV) dark matter. Possible signal channels available for readout in this medium include prompt photons, triplet excimers, and roton and phonon quasiparticles. The relative yield of these signals has implications for the sensitivity and discrimination power of a superfluid $^4$He dark matter detector. Using a 16~cm$^3$ volume of 1.75~K superfluid $^4$He read out by six immersed photomultiplier tubes, we measured the scintillation from electronic recoils ranging between 36.3 and 185~keV$_\mathrm{ee}$, yielding a mean signal size of $1.25^{+0.03}_{-0.03}$~phe/keV$_\mathrm{ee}$, and nuclear recoils from 53.2 to 1090~keV$_\mathrm{nr}$. We compare the results of our relative scintillation yield measurements to an existing semiempirical model based on helium-helium and electron-helium interaction cross sections. As a result, we also study the behavior of delayed scintillation components as a function of recoil type and energy, a further avenue for signal discrimination in superfluid $^4$He.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗