scholarly journals Scaling studies for deep learning in Liquid Argon Time Projection Chamber event classification

2019 ◽  
Vol 214 ◽  
pp. 06016
Author(s):  
Jan Strube ◽  
Kolahal Bhattacharya ◽  
Eric Church ◽  
Jeff Daily ◽  
Schram Malachi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Time Projection Chamber ◽  
Liquid Argon ◽  
Classification Problem ◽  
Classification Performance ◽  
Network Architectures ◽  
Neutrino Detectors ◽  
Large Size ◽  
Learning Techniques ◽  
Time Projection

Measurements in Liquid Argon Time Projection Chamber neutrino detectors feature large, high fidelity event images. Deep learning techniques have been extremely successful in classification tasks of photographs, but their application to these event images is challenging, due to the large size of the events, more two orders of magnitude larger than images found in classical challenges like MNIST or ImageNet. This leads to extremely long training cycles, which slow down the exploration of new network architectures and hyperpa-rameter scans to improve the classification performance. We present studies of scaling an LArTPC classification problem on multiple architectures, spanning multiple nodes. The studies are carried out in simulated events in the Micro-BooNE detector.

scholarly journals Cosmic Ray Background Removal With Deep Neural Networks in SBND

2021 ◽  
Vol 4 ◽  
Author(s):  
R. Acciarri ◽  
C. Adams ◽  
C. Andreopoulos ◽  
J. Asaadi ◽  
M. Babicz ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cosmic Ray ◽  
Liquid Argon ◽  
Short Baseline ◽  
Near Surface ◽  
Particle Count ◽  
Background Removal ◽  
Time Projection Chambers ◽  
Learning Techniques ◽  
Time Projection

In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons, and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded. In practice, this means that data from surface liquid argon time projection chambers will be dominated by cosmic particles, both as a source of event triggers and as the majority of the particle count in true neutrino-triggered events. In this work, we demonstrate a novel application of deep learning techniques to remove these background particles by applying deep learning on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, on a pixel-by-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions.

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

2022 ◽  
Vol 17 (01) ◽  
pp. P01018
Author(s):  
R. Acciarri ◽  
B. Baller ◽  
V. Basque ◽  
C. Bromberg ◽  
F. Cavanna ◽  
...  
Keyword(s):  
Deep Learning ◽  
Time Projection Chamber ◽  
Region Of Interest ◽  
Liquid Argon ◽  
Low Energy ◽  
Noise Model ◽  
Noise Mitigation ◽  
Low Energies ◽  
Exotic Physics ◽  
Time Projection

Abstract 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.

scholarly journals Deep Learning for Laryngopharyngeal Reflux Diagnosis

2021 ◽  
Vol 11 (11) ◽  
pp. 4753
Author(s):  
Gen Ye ◽  
Chen Du ◽  
Tong Lin ◽  
Yan Yan ◽  
Jack Jiang
Keyword(s):  
Deep Learning ◽  
Speech Processing ◽  
Data Augmentation ◽  
Laryngopharyngeal Reflux ◽  
Ph Monitoring ◽  
Binary Classification ◽  
Classification Problem ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Auc Value

(1) Background: Deep learning has become ubiquitous due to its impressive performance in various domains, such as varied as computer vision, natural language and speech processing, and game-playing. In this work, we investigated the performance of recent deep learning approaches on the laryngopharyngeal reflux (LPR) diagnosis task. (2) Methods: Our dataset is composed of 114 subjects with 37 pH-positive cases and 77 control cases. In contrast to prior work based on either reflux finding score (RFS) or pH monitoring, we directly take laryngoscope images as inputs to neural networks, as laryngoscopy is the most common and simple diagnostic method. The diagnosis task is formulated as a binary classification problem. We first tested a powerful backbone network that incorporates residual modules, attention mechanism and data augmentation. Furthermore, recent methods in transfer learning and few-shot learning were investigated. (3) Results: On our dataset, the performance is the best test classification accuracy is 73.4%, while the best AUC value is 76.2%. (4) Conclusions: This study demonstrates that deep learning techniques can be applied to classify LPR images automatically. Although the number of pH-positive images used for training is limited, deep network can still be capable of learning discriminant features with the advantage of technique.

A large scale purification system for a liquid argon time projection chamber

1987 ◽  
Vol 258 (2) ◽  
pp. 170-176 ◽  
Author(s):  
Peter J. Doe ◽  
Richard C. Allen ◽  
Steven D. Biller ◽  
Gerhard Bühler ◽  
Wayne A. Johnson ◽  
...  
Keyword(s):  
Time Projection Chamber ◽  
Large Scale ◽  
Liquid Argon ◽  
Purification System ◽  
Time Projection

scholarly journals Trends in Computer-Aided Diagnosis Using Deep Learning Techniques: A Review of Recent Studies on Algorithm Development

2017 ◽  
Author(s):  
Bosede Iyiade Edwards ◽  
Nosiba Hisham Osman Khougali ◽  
Adrian David Cheok
Keyword(s):  
Deep Learning ◽  
Human Subjects ◽  
Binary Classification ◽  
Computer Aided Diagnosis ◽  
Network Architectures ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Computer Aided ◽  
Multi Class Classification ◽  
Aided Diagnosis

With recent focus on deep neural network architectures for development of algorithms for computer-aided diagnosis (CAD), we provide a review of studies within the last 3 years (2015-2017) reported in selected top journals and conferences. 29 studies that met our inclusion criteria were reviewed to identify trends in this field and to inform future development. Studies have focused mostly on cancer-related diseases within internal medicine while diseases within gender-/age-focused fields like gynaecology/pediatrics have not received much focus. All reviewed studies employed image datasets, mostly sourced from publicly available databases (55.2%) and few based on data from human subjects (31%) and non-medical datasets (13.8%), while CNN architecture was employed in most (70%) of the studies. Confirmation of the effect of data manipulation on quality of output and adoption of multi-class rather than binary classification also require more focus. Future studies should leverage collaborations with medical experts to aid future with actual clinical testing with reporting based on some generally applicable index to enable comparison. Our next steps on plans for CAD development for osteoarthritis (OA), with plans to consider multi-class classification and comparison across deep learning approaches and unsupervised architectures were also highlighted.

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