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Register a new userSimulating hadron test beams in liquid argon
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Authors
Alexander Friedland
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Thorough modeling of the physics involved in liquid argon calorimetry is essential for accurately predicting the performance of DUNE and optimizing its design and analysis pipeline. At the fundamental level, it is essential to quantify the detector response to individual hadrons---protons, charged pions, and neutrons---at different injection energies. We report such a simulation, analyzed under different assumptions about event reconstruction, such as particle identification and neutron detection. The role of event containment is also quantified. The results of this simulation can help inform the ProtoDUNE test-beam data analysis, while also providing a framework for assessing the impact of various cross section uncertainties.
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Available estimates for the energy resolution of DUNE vary by as much as a factor of four. To address this controversy, and to connect the resolution to the underlying physical processes, we build an independent simulation pipeline for neutrino events in liquid argon, combining the public tools GENIE and FLUKA. Using this pipeline, we first characterize the channels of non-hermeticity of DUNE, including subthreshold particles, charge recombination, and nuclear breakup. Particular attention is paid to the role of neutrons, which are responsible for a large fraction of missing energy in all channels. Next, we determine energy resolution, by quantifying event-to-event stochastic fluctuations in missing energy. This is done for several sets of assumptions about the reconstruction performance, including those available in the literature. The resulting migration matrices, connecting true and reconstructed neutrino energies, are presented. Finally, we quantify the impact of different improvements on the experimental performance. For example, we show that dropping particle identification information degrades the resolution by a factor of two, while omitting charge deposits from de-excitation gammas worsens it by about 25%. In the future, this framework can be used to assess the impact of cross section uncertainties on the oscillation sensitivity.
The ARAPUCA is a novel concept for liquid argon scintillation light detection which has been proposed for the photon detection system of the Deep Underground Neutrino Experiment. The test in liquid argon of one of the first ARAPUCA prototypes is presented in this work, where the working principle is experimentally demonstrated. The prototype has an acceptance window of 9 cm$^2$ and is read-out by a single SiPM with active area of 0.36 cm$^2$. Its global detection efficiency was estimated by exposing it to a $^{238}U$ $alpha$ source and to cosmic rays and was found to be 1.15% $pm$ 0.15%, in good agreement with the prediction of a detailed Monte Carlo simulation of the device. Several other ARAPUCA prototypes of bigger dimensions and read-out by arrays of SiPMs have been built and are actually under test. In particular 32 ARAPUCA cells have been installed inside the protoDUNE detector, which is being assembled at CERN and will be operated in the second half of 2018.
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Scintillation from noble gases is an important technique in particle physics including neutrino beam experiments, neutrino-less double beta-decay and dark matter searches. In liquid argon, the possibility of enhancing the light yield by the addition of a small quantity of xenon (doping at 10-1000 ppm) has been of particular interest. While the pathway for energy transfer between argon and xenon excimers is well known, the time-dependence of the process has not been fully studied in the context of a physics-based model. In this paper we present a model of the energy transfer process together with a fit to xenon-doped argon data. We have measured the diffusion limited rate constant as a function of xenon dopant. We find that the time dependence of the energy transfer is consistent with diffusion-limited reactions. Additionally, we find that commercially obtained argon can have a small xenon component (4 ppm). Our result will facilitate the use of xenon-doped liquid argon in future experiments.
The Liquid Argon Time Projection Chamber (LArTPC) is an advanced neutrino detector technology widely used in recent and upcoming accelerator neutrino experiments. It features a low energy threshold and high spatial resolution that allow for comprehensive reconstruction of event topologies. In current-generation LArTPCs, the recorded data consist of digitized waveforms on wires produced by induced signal on wires of drifting ionization electrons, which can also be viewed as two-dimensional (2D) (time versus wire) projection images of charged-particle trajectories. For such an imaging detector, one critical step is the signal processing that reconstructs the original charge projections from the recorded 2D images. For the first time, we introduce a deep neural network in LArTPC signal processing to improve the signal region of interest detection. By combining domain knowledge (e.g., matching information from multiple wire planes) and deep learning, this method shows significant improvements over traditional methods. This work details the method, software tools, and performance evaluated with realistic detector simulations.
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