scholarly journals Demonstration of MeV-scale physics in liquid argon time projection chambers using ArgoNeuT

2019 ◽  
Vol 99 (1) ◽  
Author(s):  
R. Acciarri ◽  
C. Adams ◽  
J. Asaadi ◽  
B. Baller ◽  
T. Bolton ◽  
...  
Keyword(s):  
Liquid Argon ◽  
Time Projection Chambers ◽  
Time Projection

scholarly journals First Demonstration of a Pixelated Charge Readout for Single-Phase Liquid Argon Time Projection Chambers

Instruments ◽  
2020 ◽  
Vol 4 (1) ◽  
pp. 9 ◽  
Author(s):  
Jonathan Asaadi ◽  
Martin Auger ◽  
Antonio Ereditato ◽  
Damian Goeldi ◽  
Umut Kose ◽  
...  
Keyword(s):  
Single Phase ◽  
Signal To Noise Ratio ◽  
Liquid Argon ◽  
Track Reconstruction ◽  
3D Tracking ◽  
3D Space ◽  
Time Projection Chambers ◽  
Event Reconstruction ◽  
Phase Liquid ◽  
Time Projection

Traditional charge readout technologies of single-phase Liquid Argon Time projection Chambers (LArTPCs) based on projective wire readout introduce intrinsic ambiguities in event reconstruction. Combined with the slow response inherent in LArTPC detectors, reconstruction ambiguities have limited their performance, until now. Here, we present a proof of principle of a pixelated charge readout that enables the full 3D tracking capabilities of LArTPCs. We characterize the signal-to-noise ratio of charge readout chain to be about 14, and demonstrate track reconstruction on 3D space points produced by the pixel readout. This pixelated charge readout makes LArTPCs a viable option for high-multiplicity environments.

scholarly journals ARAPUCA light trap for large liquid argon time projection chambers

2018 ◽  
Vol 1143 ◽  
pp. 012003
Author(s):  
H da Motta ◽  
A A Machado ◽  
L Paulucci ◽  
E Segreto ◽  
A Fauth ◽  
...  
Keyword(s):  
Light Trap ◽  
Liquid Argon ◽  
Time Projection Chambers ◽  
Time Projection

scholarly journals Benefits of MeV-scale reconstruction capabilities in large liquid argon time projection chambers

2020 ◽  
Vol 102 (9) ◽  
Author(s):  
W. Castiglioni ◽  
W. Foreman ◽  
B. R. Littlejohn ◽  
M. Malaker ◽  
I. Lepetic ◽  
...  
Keyword(s):  
Liquid Argon ◽  
Time Projection Chambers ◽  
Time Projection

scholarly journals Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers

2021 ◽  
Vol 251 ◽  
pp. 03054 ◽  
Author(s):  
Jeremy Hewes ◽  
Adam Aurisano ◽  
Giuseppe Cerati ◽  
Jim Kowalkowski ◽  
Claire Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Message Passing ◽  
Liquid Argon ◽  
Great Promise ◽  
Muon Track ◽  
Particle Type ◽  
Novel Technique ◽  
Time Projection Chambers ◽  
Time Projection ◽  
The Relationship

This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise for similar reconstruction tasks in the LHC. In this paper, a multihead attention message passing network is used to classify the relationship between detector hits by labelling graph edges, determining whether hits were produced by the same underlying particle, and if so, the particle type. The trained model is 84% accurate overall, and performs best on the EM shower and muon track classes. The model’s strengths and weaknesses are discussed, and plans for developing this technique further are summarised.

scholarly journals AVOLAR. A high voltage generator for liquid argon time projection chambers

2020 ◽  
Vol 15 (03) ◽  
pp. C03057-C03057
Author(s):  
L. Romero ◽  
J.M. Cela ◽  
E. Sanchez Garcia ◽  
M. Daniel ◽  
M. de Prado
Keyword(s):  
High Voltage ◽  
Liquid Argon ◽  
Time Projection Chambers ◽  
Voltage Generator ◽  
Time Projection ◽  
High Voltage Generator

scholarly journals NEUTRINOS FROM ICARUS

2013 ◽  
Vol 53 (A) ◽  
pp. 776-781
Author(s):  
Christian Farnese
Keyword(s):  
3D Imaging ◽  
Sterile Neutrino ◽  
Rare Event ◽  
Liquid Argon ◽  
Good Energy Resolution ◽  
Time Projection Chambers ◽  
Event Reconstruction ◽  
The Status ◽  
Good Energy ◽  
Time Projection

Liquid Argon Time Projection Chambers are very promising detectors for neutrino and astroparticle physics due to their high granularity, good energy resolution and 3D imaging, allowing for a precise event reconstruction. ICARUS T600 is the largest liquid Argon (LAr) TPC detector ever built (~600 ton LAr mass) and is presently operating underground at the LNGS laboratory. This detector, internationally considered as the milestone towards the realization of the next generation of massive detectors (~tens of ktons) for neutrino and rare event physics, has been smoothly running since summer 2010, collecting data with the CNGS beam and with cosmics. The status of this detector will be shortly described together with the intent to adopt the LAr TPC technology at CERN as a possible solution to the sterile neutrino puzzle.

scholarly journals A 4 tonne demonstrator for large-scale dual-phase liquid argon time projection chambers

2018 ◽  
Vol 13 (11) ◽  
pp. P11003-P11003 ◽  
Author(s):  
B. Aimard ◽  
Ch. Alt ◽  
J. Asaadi ◽  
M. Auger ◽  
V. Aushev ◽  
...  
Keyword(s):  
Large Scale ◽  
Liquid Argon ◽  
Dual Phase ◽  
Time Projection Chambers ◽  
Phase Liquid ◽  
Time Projection

A pixelated charge readout for Liquid Argon Time Projection Chambers

2018 ◽  
Vol 13 (02) ◽  
pp. C02008-C02008 ◽  
Author(s):  
J. Asaadi ◽  
M. Auger ◽  
A. Ereditato ◽  
D. Goeldi ◽  
R. Hänni ◽  
...  
Keyword(s):  
Liquid Argon ◽  
Time Projection Chambers ◽  
Time Projection

scholarly journals Neural-network-driven proton decay sensitivity in the p → $$ \overline{\nu} $$K+ channel using large liquid argon time projection chambers

2021 ◽  
Vol 2021 (4) ◽  
Author(s):  
C. Alt ◽  
B. Radics ◽  
A. Rubbia
Keyword(s):  
Neural Network ◽  
Liquid Argon ◽  
Proton Decay ◽  
K Channel ◽  
Detector Response ◽  
Event Generator ◽  
Decay Modes ◽  
Time Projection Chambers ◽  
Phase Liquid ◽  
Time Projection

Abstract We report on an updated sensitivity for proton decay via p → $$ \overline{\nu} $$ ν ¯ K+ at large, dual phase liquid argon time projection chambers (LAr TPCs). Our work builds on a previous study in which several nucleon decay modes have been simulated and analyzed [1]. At the time several assumptions were needed to be made on the detector and the backgrounds. Since then, the community has made progress in defining these, and the computing power available enables us to fully simulate and reconstruct large samples in order to perform a better estimate of the sensitivity to proton decay. In this work, we examine the benchmark channel p → $$ \overline{\nu} $$ ν ¯ K+, which was previously found to be one of the cleanest channels. Using an improved neutrino event generator and a fully simulated LAr TPC detector response combined with a dedicated neural network for kaon identification, we demonstrate that a lifetime sensitivity of τ /Br (p → $$ \overline{\nu} $$ ν ¯ K+) > 7 × 1034 years at 90% confidence level can be reached at an exposure of 1 megaton · year in quasi-background-free conditions, confirming the superiority of the LAr TPC over other technologies to address the challenging proton decay modes.

scholarly journals Reconstruction for Liquid Argon TPC Neutrino Detectors Using Parallel Architectures

2020 ◽  
Vol 245 ◽  
pp. 02012
Author(s):  
Sophie Berkman ◽  
Giuseppe Cerati ◽  
Brian Gravelle ◽  
Boyana Norris ◽  
Allison Reinsvold Hall ◽  
...  
Keyword(s):  
Charged Particles ◽  
Liquid Argon ◽  
Parallel Architectures ◽  
Reconstruction Algorithms ◽  
Computing Power ◽  
Neutrino Detectors ◽  
Time Projection Chambers ◽  
And Performance ◽  
Time Required ◽  
Time Projection

Neutrinos are particles that interact rarely, so identifying them requires large detectors which produce lots of data. Processing this data with the computing power available is becoming more difficult as the detectors increase in size to reach their physics goals. In liquid argon time projection chambers (TPCs) the charged particles from neutrino interactions produce ionization electrons which drift in an electric field towards a series of collection wires, and the signal on the wires is used to reconstruct the interaction. The MicroBooNE detector currently collecting data at Fermilab has 8000 wires, and planned future experiments like DUNE will have 100 times more, which means that the time required to reconstruct an event will scale accordingly. Modernization of liquid argon TPC reconstruction code, including vectorization, parallelization and code portability to GPUs, will help to mitigate these challenges. The liquid argon TPC hit finding algorithm within the LArSoft framework used across multiple experiments has been vectorized and parallelized. This increases the speed of the algorithm on the order of ten times within a standalone version on Intel architectures. This new version has been incorporated back into LArSoft so that it can be generally used. These methods will also be applied to other low-level reconstruction algorithms of the wire signals such as the deconvolution. The applications and performance of this modernized liquid argon TPC wire reconstruction will be presented.

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