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Register a new userBenefits of MeV-scale reconstruction capabilities in large liquid argon time projection chambers
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Using truth-level Monte Carlo simulations of particle interactions in a large volume of liquid argon, we demonstrate physics capabilities enabled by reconstruction of topologically compact and isolated low-energy features, or `blips, in large liquid argon time projection chamber (LArTPC) events. These features are mostly produced by electron products of photon interactions depositing ionization energy. The blip identification capability of the LArTPC is enabled by its unique combination of size, position resolution precision, and low energy thresholds. We show that consideration of reconstructed blips in LArTPC physics analyses can result in substantial improvements in calorimetry for neutrino and new physics interactions and for final-state particles ranging in energy from the MeV to the GeV scale. Blip activity analysis is also shown to enable discrimination between interaction channels and final-state particle types. In addition to demonstrating these gains in calorimetry and discrimination, some limitations of blip reconstruction capabilities and physics outcomes are also discussed.
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A 10 kilo-tonne dual-phase liquid argon TPC is one of the detector options considered for the Deep Underground Neutrino Experiment (DUNE). The detector technology relies on amplification of the ionisation charge in ultra-pure argon vapour and oers several advantages compared to the traditional single-phase liquid argon TPCs. A 4.2 tonne dual-phase liquid argon TPC prototype, the largest of its kind, with an active volume of 3x1x1 $m^3$ has been constructed and operated at CERN. In this paper we describe in detail the experimental setup and detector components as well as report on the operation experience. We also present the first results on the achieved charge amplification, prompt scintillation and electroluminescence detection, and purity of the liquid argon from analyses of a collected sample of cosmic ray muons.
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, idealized 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.
We develop a novel approach for a Time Projection Chamber (TPC) concept suitable for deployment in kilotonne scale detectors, with a charge-readout system free from reconstruction ambiguities, and a robust TPC design that reduces high-voltage risks while increasing the coverage of the light collection system. This novel concept could be deployed as a Far Detector module in the Deep Underground Neutrino Experiment (DUNE) neutrino-oscillation experiment. For the charge-readout system, we use the charge-collection pixels and associated application-specific integrated circuits currently being developed for the liquid argon (LAr) component of the DUNE Near Detector design, ArgonCube. In addition, we divide the TPC into a number or shorter drift volumes, reducing the total voltage used to drift the ionisation electrons, and minimising the stored energy per TPC. Segmenting the TPC also contains scintillation light, allowing for precise trigger localisation and a more expansive light-readout system. Furthermore, the design opens the possibility of replacing or upgrading components. These augmentations could substantially improve reliability and sensitivity, particularly for low energy signals, in comparison to a traditional monolithic LArTPCs with projective charge-readout.
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.
Large volume Liquid Argon Time Projection Chambers (LAr-TPC) are used and proposed for neutrino physics and rare event search. Most of these detectors make use of the scintillation light of liquid argon for trigger purposes. Two different approaches can be adopted to provide these detectors with an effective trigger system, relying upon analog or digital processing of signal coming from photodetectors, like photomultiplier tubes or silicon photomultipliers. Each method presents advantages and drawbacks, so the implementation of a hybrid solution can benefit from both approaches. To this purpose, an innovative electronic board prototype has been designed and proposed for the use in large volume LAr-TPC detectors.
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