Space Charge Effects in Noble-Liquid Calorimeters and Time Projection Chambers

Palestini, S.

doi:10.3390/instruments5010009

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…In order to exclude any space charge effect from the positive ions, the analysis procedure sketched in Palestini and McDonald (2023) was applied. It follows, as discussed in Supplementary Appendix (Section A), that field distortions once the current reached saturation (full collection) were typically at the 5%-level or below (with a maximum field distortion of 15%) during the measurements.…”

Section: Methodsmentioning

confidence: 99%

Observation of strong wavelength-shifting in the argon-tetrafluoromethane system

Amedo,

González-Díaz,

Brunbauer

et al. 2023

Front. Detect. Sci. Technol.

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We report the scintillation spectra of Ar/CF4 mixtures in the range 210–800 nm, obtained under X-ray irradiation for various pressures (1–5 bar) and concentrations (0%–100%). Special care was taken to eliminate effects related to space charge and charge recombination, so that results can be extrapolated following conventional wisdom to those expected for minimum ionizing particles under the typical electric fields employed in gaseous instrumentation. Our study sheds light into the microscopic pathways leading to scintillation in this family of mixtures and reinvigorates the prospects of use in next-generation scintillation-based chambers.

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Section: Methodsmentioning

confidence: 99%

Observation of strong wavelength-shifting in the argon-tetrafluoromethane system

Amedo,

González-Díaz,

Brunbauer

et al. 2023

Front. Detect. Sci. Technol.

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“…Figure 2 shows the hits from one APA in a simulated ProtoDUNE-SP event and the three zoomed regions give example 48 × 48 pixel patches in the track, shower and Michel categories. Detector effects such as the ones introduced by space charge [1,22] are included in the simulation. The images from the three wire planes are independently evaluated.…”

Section: The Convolutional Neural Networkmentioning

confidence: 99%

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

et al. 2022

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Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation.

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“…Figure 3 shows the hits from one APA in a simulated ProtoDUNE-SP event and the three zoomed regions give example 48 × 48 pixel patches in the track, shower and Michel categories. Detector effects such as the ones introduced by space charge [1,22] Fig. 2: The CNN architecture.…”

Section: The Convolutional Neural Networkmentioning

confidence: 99%

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Abud,

Abi,

Acciarri

et al. 2022

Preprint

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Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track-and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation.

show abstract

Space Charge Effects in Noble-Liquid Calorimeters and Time Projection Chambers

Cited by 4 publications

References 20 publications

Observation of strong wavelength-shifting in the argon-tetrafluoromethane system

Observation of strong wavelength-shifting in the argon-tetrafluoromethane system

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

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