Extracting low energy signals from raw LArTPC waveforms using deep learning techniques — A proof of concept

Uboldi, L.; David, Ruth; Andrews, Michael Benjamin; Wang, Michael; Wenzel, Hans-Joachim; Wu, W.; Yang, T.

doi:10.1016/j.nima.2022.166371

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…This method has the disadvantage of discarding signals below certain energies. Recently, a novel deep-learning approach based on the application of a simple one-dimensional convolutional neural network (1D-CNN) has been developed, which shows a very encouraging ability to extract small signals and offers great potential for low-energy physics [141,144]. Figure 18 shows the comparisons of the ROI efficiencies of the two ROI finders for the induction plane and the collection plane from ArgoNeuT.…”

Section: Low-level Charge Signal Reconstructionmentioning

confidence: 99%

“…Finally, other online data selection methods, such as ones employing machine learning algorithms, are also being investigated for application in LArTPCs [12], especially at low energies where differences between signal and background become more subtle. In particular, CNNs have been studied as a way of improving the trigger efficiency of LArTPCs in real-time and online data processing within the TDAQ system [144,151]. For example, 2D CNNs have for forming a trigger activity object from simulated trigger primitives in a single DUNE supernova neutrino interaction event, as a function of visible energy of the outgoing electron in the interaction [11].…”

Section: Trigger and Data Selection Strategiesmentioning

confidence: 99%

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Low-energy physics in neutrino LArTPCs

Caratelli¹,

Foreman²,

Friedland³

et al. 2023

J. Phys. G: Nucl. Part. Phys.

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In this paper, we review scientific opportunities and challenges related to detection and reconstruction of low-energy (less than 100 MeV) signatures in liquid argon time-projection chamber (LArTPC) neutrino detectors. LArTPC neutrino detectors designed for performing precise long-baseline oscillation measurements with GeV-scale accelerator neutrino beams also have unique sensitivity to a range of physics and astrophysics signatures via detection of event features at and below the few tens of MeV range. In addition, low-energy signatures are an integral part of GeV-scale accelerator neutrino interaction final-states, and their reconstruction can enhance the oscillation physics sensitivities of LArTPC experiments. New physics signals from accelerator and natural sources also generate diverse signatures in the low-energy range, and reconstruction of these signatures can increase the breadth of Beyond the Standard Model scenarios accessible in LArTPC-based searches. A variety of experimental and theory-related challenges remain to realizing this full range of potential benefits. Neutrino interaction cross-sections and other nuclear physics processes in argon relevant to sub-hundred-MeV LArTPC signatures are poorly understood, and improved theory and experimental measurements are needed; pion decay-at-rest sources and charged particle and neutron test beams are ideal facilities for improving this understanding. There are specific calibration needs in the low-energy range, as well as specific needs for control and understanding of radiological and cosmogenic backgrounds. Low-energy signatures, whether steady-state or part of a supernova burst or larger GeV-scale event topology, have specific triggering, DAQ and reconstruction requirements that must be addressed outside the scope of conventional GeV-scale data collection and analysis pathways. Novel concepts for future LArTPC technology that enhance low-energy capabilities should also be explored to help address these challenges.

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Section: Low-level Charge Signal Reconstructionmentioning

confidence: 99%

Section: Trigger and Data Selection Strategiesmentioning

confidence: 99%

Low-energy physics in neutrino LArTPCs

Caratelli¹,

Foreman²,

Friedland³

et al. 2023

J. Phys. G: Nucl. Part. Phys.

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“…In this paper, we introduce a novel deep-learning approach based on the application of a simple one-dimensional convolutional neural network (1D-CNN) to the task of finding regions-of-interest (ROIs) in minimally processed LArTPC waveforms, as described in ref. [9]. Deep learning techniques are widely used in high energy physics and play a significant role in the reconstruction of neutrino interactions.…”

Section: Introductionmentioning

confidence: 99%

A deep-learning based raw waveform region-of-interest finder for the liquid argon time projection chamber

Acciarri¹,

Baller²,

Basque³

et al. 2022

J. Inst.

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The liquid argon time projection chamber (LArTPC) detector technology has an excellent capability to measure properties of low-energy neutrinos produced by the sun and supernovae and to look for exotic physics at very low energies. In order to achieve those physics goals, it is crucial to identify and reconstruct signals in the waveforms recorded on each TPC wire. In this paper, we report on a novel algorithm based on a one-dimensional convolutional neural network (CNN) to look for the region-of-interest (ROI) in raw waveforms. We test this algorithm using data from the ArgoNeuT experiment in conjunction with an improved noise mitigation procedure and a more realistic data-driven noise model for simulated events. This deep-learning ROI finder shows promising performance in extracting small signals and gives an efficiency approximately twice that of the traditional algorithm in the low energy region of ∼0.03–0.1 MeV. This method offers great potential to explore low-energy physics using LArTPCs.

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Analysis Strategy

Andrews¹

2023

Springer Theses

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Extracting low energy signals from raw LArTPC waveforms using deep learning techniques — A proof of concept

Cited by 6 publications

References 17 publications

Low-energy physics in neutrino LArTPCs

Low-energy physics in neutrino LArTPCs

A deep-learning based raw waveform region-of-interest finder for the liquid argon time projection chamber

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