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1.
A pixelated charge readout for Liquid Argon Time Projection Chambers / Asaadi, J (Texas U., Arlington) ; Auger, M (U. Bern, AEC) ; Ereditato, A (U. Bern, AEC) ; Goeldi, D (U. Bern, AEC) ; Hänni, R (U. Bern, AEC) ; Kose, U (CERN) ; Kreslo, I (U. Bern, AEC) ; Lorca, D (U. Bern, AEC) ; Luethi, M (U. Bern, AEC) ; Rudolf von Rohr, C (U. Bern, AEC) et al.
Liquid Argon Time Projection Chambers (LArTPCs) are ideally suited to perform long-baseline neutrino experiments aiming to measure CP violation in the lepton sector, and determine the ordering of the three neutrino mass eigenstates. LArTPCs have used projective wire readouts for charge detection since their conception in 1977. [...]
CERN-ACC-2018-204.- 2018 - 8 p. - Published in : JINST 13 (2018) C02008
In : Light Detection in Noble Elements, Menlo Park, CA, USA, 22 - 24 Sep 2017, pp.C02008
2.
First Demonstration of a Pixelated Charge Readout for Single-Phase Liquid Argon Time Projection Chambers / Asaadi, Jonathan (Texas U., Arlington) ; Auger, Martin (U. Bern, AEC) ; Ereditato, Antonio (U. Bern, AEC) ; Goeldi, Damian (U. Bern, AEC) ; Kose, Umut (CERN) ; Kreslo, Igor (U. Bern, AEC) ; Lorca, David (U. Bern, AEC) ; Luethi, Matthias (U. Bern, AEC) ; Rudolf von Rohr, Christoph Benjamin Urs (U. Bern, AEC) ; Sinclair, James (U. Bern, AEC) et al.
Liquid Argon Time Projection Chambers (LArTPCs) have been selected for the future long-baseline Deep Underground Neutrino Experiment (DUNE). To allow LArTPCs to operate in the high-multiplicity near detector environment of DUNE, a new charge readout technology is required. [...]
arXiv:1801.08884.- 2020-03-23 - 13 p. - Published in : Instruments 4 (2020) 9 Fulltext: fulltext1650599 - PDF; instruments-04-00009 - PDF; Preprint: PDF;
3.
The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector / MicroBooNE Collaboration
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. [...]
AIDA-2020-PUB-2017-002; arXiv:1708.03135; FERMILAB-PUB-17-306-ND.- Geneva : CERN, 2018 - 25 p. - Published in : Eur. Phys. J. C 78 (2018) 82 Fulltext: PDF;

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