Testing of High Voltage Surge Protection Devices for Use in Liquid Argon TPC Detectors

In this paper we demonstrate the capability of high voltage varistors and gas discharge tube arrestors for use as surge protection devices in liquid argon time projection chamber detectors. The insulating and clamping behavior of each type of device is characterized in air (room temperature), and liquid argon (90~K), and their robustness under high voltage and high energy surges in cryogenic conditions is verified. The protection of vulnerable components in liquid argon during a 150 kV high voltage discharge is also demonstrated. Each device is tested for argon contamination and light emission effects, and both are constrained to levels where no significant impact upon liquid argon time projection chamber functionality is expected. Both devices investigated are shown to be suitable for HV surge protection applications in cryogenic detectors.Comment: 22 pages, 18 figures v2: reduced file size for journal submissio

Muon-Induced Background Study for an Argon-Based Long Baseline Neutrino Experiment

We evaluated rates of transversing muons, muon-induced fast neutrons, and production of $^{40}$Cl and other cosmogenically produced nuclei that pose as potential sources of background to the physics program proposed for an argon-based long baseline neutrino experiment at the Sanford Underground Research Facility (SURF). The Geant4 simulations were carried out with muons and muon-induced neutrons for both 800 ft (0.712 km.w.e.) and 4850 ft levels (4.3 km.w.e.). We developed analytic models to independently calculate the $^{40}$Cl production using the measured muon fluxes at different levels of the Homestake mine. The muon induced $^{40}$Cl production rates through stopped muon capture and the muon-induced neutrons and protons via (n,p) and (p,n) reactions were evaluated. We find that the Monte Carlo simulated production rates of $^{40}$Cl agree well with the predictions from analytic models. A depth-dependent parametrization was developed and benchmarked to the direct analytic models. We conclude that the muon-induced processes will result in large backgrounds to the physics proposed for an argon-based long baseline neutrino experiment at a depth of less than 4.0 km.w.e.Comment: 12 pages, 15 figure

Hydrostatic Level Sensors as High Precision Ground Motion Instrumentation for Tevatron and Other Energy Frontier Accelerators

Particle accelerators pushed the limits of our knowledge in search of the answers to most fundamental questions about micro-world and our Universe. In these pursuits, accelerators progressed to higher and higher energies and particle beam intensities as well as increasingly smaller and smaller beam sizes. As the result, modern existing and planned energy frontier accelerators demand very tight tolerances on alignment and stability of their elements: magnets, accelerating cavities, vacuum chambers, etc. In this article we describe the instruments developed for and used in such accelerators as Fermilab's Tevatron (FNAL, Batavia, IL USA) and for the studies toward an International Linear Collider (ILC). The instrumentation includes Hydrostatic Level Sensors (HLS) for very low frequency measurements. We present design features of the sensors, outline their technical parameters, describe test and calibration procedures and discuss different regimes of operation. Experimental results of the ground motion measurements with these detectors will be presented in subsequent paper

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector.

The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies

Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber

We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

The development and operation of Liquid-Argon Time-Projection Chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.Comment: Preprint to be submitted to The European Physical Journal