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تسجيل مستخدم جديدReconstruction for Liquid Argon TPC Neutrino Detectors Using Parallel Architectures
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Sophie Berkman
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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 texttt{LArSoft}xspace 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 texttt{LArSoft}xspace 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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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 even more difficult as the detectors increase in size to reach
their physics goals. Liquid argon time projection chamber (LArTPC) neutrino experiments are expected to grow in the next decade to have 100 times more wires than in currently operating experiments, and modernization of LArTPC reconstruction code, including parallelization both at data- and instruction-level, will help to mitigate this challenge. The LArTPC hit finding algorithm is used across multiple experiments through a common software framework. In this paper we discuss a parallel implementation of this algorithm. Using a standalone setup we find speed up factors of two times from vectorization and 30--100 times from multi-threading on Intel architectures. The new version has been incorporated back into the framework so that it can be used by experiments. On a serial execution, the integrated version is about 10 times faster than the previous one and, once parallelization is enabled, further speedups comparable to the standalone program are achieved.
Impurities in noble liquid detectors used for neutrino and dark matter experiments can significantly impact the quality of data. We present an experimentally verified model for describing the dynamics of impurity distributions in liquid argon (LAr) d
etectors. The model considers sources, sinks, and transport of impurities within and between the gas and liquid argon phases. Measurements of oxygen concentrations in a 20-L LAr multi-purpose test stand are compared to calculations made with this model to show that an accurate description of the concentrations under various operational conditions can be obtained. A result of this analysis is a determination of Henrys coefficient for oxygen in LAr. These calculations also show that some processes have small effects on the impurity dynamics and excluding them yields a solution as a sum of two exponential terms. This solution provides a simple way to extract Henrys coefficient with negligible approximation error. It is applied to the data and the Henrys coefficient for oxygen in LAr is obtained as 0.84$^{+0.09}_{-0.05}$, consistent with literature results. Based on the analysis of the data with the model, we further suggest that, for a large liquid argon detector, barriers to flow (baffles) installed in the gas phase to restrict flow can help reduce the ultimate impurity concentration in the LAr.
In this paper we explore the potential improvements in neutrino event reconstruction that a 3D pixelated readout could offer over a 2D projective wire readout for liquid argon time projection chambers. We simulate and study events in two generic, ide
alized detector configurations for these two designs, classifying events in each sample with deep convolutional neural networks to compare the best 2D results to the best 3D results. In almost all cases we find that the 3D readout provides better reconstruction efficiency and purity than the 2D projective wire readout, with the advantages of 3D being particularly evident in more complex topologies, such as electron neutrino charged current events. We conclude that the use of a 3D pixelated detector could significantly enhance the reach and impact of future liquid argon TPC experiments physics program, such as DUNE.
The ICARUS T600 liquid argon (LAr) time projection chamber (TPC) underwent a major overhaul at CERN in 2016-2017 to prepare for the operation at FNAL in the Short Baseline Neutrino (SBN) program. This included a major upgrade of the photo-multiplier
system and of the TPC wire read-out electronics. The full TPC wire read-out electronics together with the new wire biasing and interconnection scheme are described. The design of a new signal feed-through flange is also a fundamental piece of this overhaul whose major feature is the integration of all electronics components onto the signal flange. Initial functionality tests of the full TPC electronics chain installed in the T600 detector at FNAL are also described.
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