Engineering Papers⌕ Search

Engineering topics

Kaneshige, N.

Publications and source records attributed to Kaneshige, N..

24 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Cosmic Ray Background Rejection with Wire-Cell LArTPC Event Reconstruction in the MicroBooNE Detector

For a large liquid-argon time-projection chamber (LArTPC) operating on or near the Earth’s surface to detect neutrino interactions, the rejection of cosmogenic background is a critical and challenging task because of the large cosmic-ray flux and the long drift time of the time-projection chamber. We introduce a superior cosmic background rejection procedure based on the Wire-Cell three-dimensional (3D) event reconstruction for LArTPCs. From an initial 1:20 000 neutrino to cosmic-ray background ratio, we demonstrate these tools on data from the MicroBooNE experiment and create a high-performance generic neutrino event selection with a cosmic contamination of 14.9% (9.7%) for a visible energy region greater than O(200) MeV. The neutrino interaction selection efficiency is 80.4% and 87.6% for inclusive ν μ charged-current and ν e charged-current interactions, respectively. Here, this significantly improved performance compared with existing reconstruction algorithms marks a major milestone toward reaching the scientific goals of LArTPC neutrino oscillation experiments operating near the Earth’s surface.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Design and calibration of an optically segmented single volume scatter camera for neutron imaging

The Optically Segmented Single Volume Scatter Camera (OS-SVSC) aims to image neutron sources for non-proliferation applications using the kinematic reconstruction of elastic double-scatter events. Our prototype system consists of 64 EJ-204 organic plastic scintillator bars, each measuring 5mm × 5mm × 200mm and individually wrapped in Teflon tape. The scintillator array is optically coupled to two silicon photomultiplier ArrayJ-60035 64P-PCB arrays, each comprised of 64 individual 6x6mm J-Series sensors arranged in an 8 × 8 array. We report on the design details, including component selections, mechanical design and assembly, and the electronics system. The described design leveraged existing off-the-shelf solutions to support the rapid development of a phase 1 prototype. Several valuable lessons were learned from component and system testing, including those related to the detector's mechanical structure and electrical crosstalk that we conclude originates in the commercial photodetector arrays and the associated custom breakout cards. We detail our calibration efforts, beginning with calibrations for the electronics, based on the IRS3D application-specific integrated circuits, and their associated timing resolutions, ranging from 3090ps. With electronics calibrations applied, energy and position calibrations were performed for a set of edge bars using 22 Na and 90 Sr, respectively, reporting an average resolution of 12.07±0.03mm for energy depositions between 900keVee and 1000keVee. We further demonstrate a position calibration method for the internal bars of the matrix using cosmic-ray muons as an alternative to emission sources that cannot easily access these bars, with an average measured resolution of 14.86+-0.29mm for depositions between 900keVee and 1000keVee. The coincident time resolution reported between pairs of bars measured up to 400ps from muon acquisitions. Energy and position calibration values measured with muons are consistent with those obtained using particle emission sources.

47 OTHER INSTRUMENTATION↗
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 ↗