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Vahle, P.

Publications and source records attributed to Vahle, P..

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The design and construction of the Chips water Cherenkov neutrino detector

Chips (CHerenkov detectors In mine PitS) was a prototype large-scale water Cherenkov detector located in northern Minnesota. The main aim of the R&D project was to demonstrate that construction costs of neutrino oscillation detectors could be reduced by at least an order of magnitude compared to other equivalent experiments. This article presents design features of the Chips detector along with details of the implementation and deployment of the prototype. While issues during and after the deployment of the detector prevented data taking, a number of key concepts and designs were successfully demonstrated.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Accelerator/Experiment Operations - FY 2018

This Technical Memorandum summarizes the Fermilab accelerator and experiment operations for FY 2018. It is one of a series of annual publications intended to gather information in one place. In this case, the information concerns the FY 2018 NOvA and MINERvA experiments using the Main Injector Neutrino Beam (NuMI), the MicroBooNE experiment, LArIAT experiment, and Meson Test Beam activities in the 120 GeV external switchyard beam (SY120).

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

Measuring the attenuation length of water in the CHIPS-M water Cherenkov detector

The water at the proposed site of the CHIPS water Cherenkov detector has been studied to measure its attenuation length for Cherenkov light as a function of filtering time. A scaled model of the CHIPS detector filled with water from the Wentworth 2W pit, proposed site of the CHIPS deployment, in conjunction with a 3.2 m vertical column filled with this water, was used to study the transmission of 405 nm laser light. Results consistent with attenuation lengths of up to 100 m were observed for this wavelength with filtration and UV sterilization alone.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

A Convolutional Neural Network Neutrino Event Classifier

Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗