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Augmented Signal Processing in Liquid Argon Time Projection Chambers with a Deep Neural Network
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Augmented Signal Processing in Liquid Argon Time Projection Chambers with a Deep Neural Network
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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.
Forward citations
Cited by 4 Pith papers
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Enhanced Ionization Charge Identification in the Short-Baseline Neutrino Program Neutrino Detectors with Deep Neural Networks
A DNN-based region of interest detection method for SBN neutrino detectors outperforms traditional wire-by-wire thresholding in identification accuracy and reconstruction quality while being more robust to performance...
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Neutron Reconstruction via Blips in Liquid Argon Time Projection Chambers
Simulation-based proof-of-concept demonstrates neutron identification and reconstruction of direction and energy using blips from inelastic scattering in LArTPCs.
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Neutron Reconstruction via Blips in Liquid Argon Time Projection Chambers
Simulation shows LArTPC blips from neutron inelastic scattering can identify neutrons and reconstruct final-state neutron direction and energy in sub-GeV neutrino interactions.
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Enhanced Ionization Charge Identification in the Short-Baseline Neutrino Program Neutrino Detectors with Deep Neural Networks
DNN ROI detection outperforms traditional wire-by-wire thresholding in identifying ionization signals in SBND and ICARUS detectors and shows greater robustness to performance variations.
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