First demonstration of O ( 1 ns ) timing resolution in the MicroBooNE liquid argon time projection chamber
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Engineering topics
Publications and source records attributed to James, C..
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The Colossus platform at Fermilab will be the largest and most powerful 3He/4He dilution-cooled cryogenic system constructed to-date. Perhaps its primary innovation will be in the integration of a liquid helium cryogenics plant to cool the stages typically cooled by mechanical cryocoolers in commercially available cryogen-free dilution refrigerators. This design shift carries with it important implications for the future of cryogenics associated with quantum computing due to the inherently greater efficiencies of helium cryogenic plants when compared to the use of multitude of independent mechanical cryocoolers. Construction of Colossus is expected to begin in 2023 with a target of commencing operations in 2025.
Colossus is a large millikelvin platform that has a two-meter diameter mixing chamber plate. This uniquely large millikelvin platform utilizes a cryogenics plant to maintain progressively lower temperatures at each of the three upper stages and precool the lower three stages of the system. At the millikelvin stages, Colossus employs multiple commercially available dilution units to achieve millikelvin temperatures at the 100 mK and 20 mK stages. The incoming helium-3/helum-4 process fluid mixture for each dilution unit flows through tubes wrapped around copper posts attached to each of the upper stages of the system. These tubes function as capillary heat exchanger to reduce the temperature of the incoming helium mixture to the appropriate temperature at each stage.
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We report the MicroBooNE liquid argon time projection chamber (LArTPC) maintains a high level of liquid argon purity through the use of a filtration system that removes electronegative contaminants in continuously-circulated liquid, recondensed boil off, and externally supplied argon gas. We use the MicroBooNE LArTPC to reconstruct MeV-scale radiological decays. Using this technique we measure the liquid argon filtration system's efficacy at removing radon. This is studied by placing a 500 kBq 222 Rn source upstream of the filters and searching for a time-dependent increase in the number of radiological decays in the LArTPC. In the context of two models for radon mitigation via a liquid argon filtration system, a slowing mechanism and a trapping mechanism, MicroBooNE data supports a radon reduction factor of greater than 97% or 99.999%, respectively. Furthermore, a radiological survey of the filters found that the copper-based filter material was the primary medium that removed the 222 Rn. This is the first observation of radon mitigation in liquid argon with a large-scale copper-based filter and could offer a radon mitigation solution for future large LArTPCs.
In this article, we describe a modified implementation of Mask Region-based Convolutional Neural Networks (Mask-RCNN) for cosmic ray muon clustering in a liquid argon TPC and applied to MicroBooNE neutrino data. Our implementation of this network, called sMask-RCNN, uses sparse submanifold convolutions to increase processing speed on sparse datasets, and is compared to the original dense version in several metrics. The networks are trained to use wire readout images from the MicroBooNE liquid argon time projection chamber as input and produce individually labeled particle interactions within the image. These outputs are identified as either cosmic ray muon or electron neutrino interactions. We find that sMask-RCNN has an average pixel clustering efficiency of 85.9% compared to the dense network's average pixel clustering efficiency of 89.1%. We demonstrate the ability of sMask-RCNN used in conjunction with MicroBooNE's state-of-the-art Wire-Cell cosmic tagger to veto events containing only cosmic ray muons. The addition of sMask-RCNN to the Wire-Cell cosmic tagger removes 70% of the remaining cosmic ray muon background events at the same electron neutrino event signal efficiency. Furthermore, this event veto can provide 99.7% rejection of cosmic ray-only background events while maintaining an electron neutrino event-level signal efficiency of 80.1%. In addition to cosmic ray muon identification, sMask-RCNN could be used to extract features and identify different particle interaction types in other 3D-tracking detectors.
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Primary challenges for current and future precision neutrino experiments using liquid argon time projection chambers (LArTPCs) include understanding detector effects and quantifying the associated systematic uncertainties. This paper presents a novel technique for assessing and propagating LArTPC detector-related systematic uncertainties. The technique makes modifications to simulation waveforms based on a parameterization of observed differences in ionization signals from the TPC between data and simulation, while remaining insensitive to the details of the detector model. The modifications are then used to quantify the systematic differences in low- and high-level reconstructed quantities. This approach could be applied to future LArTPC detectors, such as those used in SBN and DUNE.
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