Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Data
Deep convolutional neural networks (CNNs) show strong promise for analyzing scientific data in many domains including particle imaging detectors such as a liquid argon time projection chamber (LArTPC). Yet the high sparsity of LArTPC data challenges traditional CNNs which were designed for dense data such as photographs. A naive application of CNNs on LArTPC data results in inefficient computations and a poor scalability to large LArTPC detectors such as the Short Baseline Neutrino Program and Deep Underground Neutrino Experiment. Recently Submanifold Sparse Convolutional Networks (SSCNs) have been proposed to address this challenge. We report their performance on a 3D semantic segmentation task on simulated LArTPC samples. In comparison with standard CNNs, we observe that the computation memory and wall-time cost for inference are reduced by factor of 364 and 33 respectively without loss of accuracy. The same factors for 2D samples are found to be 93 and 3.1 respectively. Using SSCN, we present the first machine learning-based approach to the reconstruction of Michel electrons using public 3D LArTPC samples. We find a Michel electron identification efficiency of 93.9\% with 98.8\% of true positive rate. Reconstructed Michel electron clusters yield 96.1\% in average pixel clustering efficiency and 97.3\% in purity. The results are compelling to show strong promise of scalable data reconstruction technique using deep neural networks for large scale LArTPC detectors.
NurtureToken New!

Token crowdsale for this paper ends in

Buy Nurture Tokens

Authors

Are you an author of this paper? Check the Twitter handle we have for you is correct.

Laura Dominé (add twitter)
Kazuhiro Terao (add twitter)
Ask The Authors

Ask the authors of this paper a question or leave a comment.

Read it. Rate it.
#1. Which part of the paper did you read?

#2. The paper contains new data or analyses that is openly accessible?
#3. The conclusion is supported by the data and analyses?
#4. The conclusion is of scientific interest?
#5. The result is likely to lead to future research?

Github
User:
Stargazers:
1
Forks:
1
Open Issues:
0
Network:
1
Subscribers:
2
Language:
Python
PyTorch implementations of dense and sparse U-ResNet
Youtube
Link:
None (add)
Views:
0
Likes:
0
Dislikes:
0
Favorites:
0
Comments:
0
Other
Sample Sizes (N=):
Inserted:
Words Total:
Words Unique:
Source:
Abstract:
None
03/14/19 06:05PM
5,598
1,858
Tweets
dlphysics: Laura Domine put up a technical demo paper of @facebookai Sparse Submanifold Conv. Net (SSCN). SSCN is a fantastic fit for #LArTPC data which is sparse but locally dense and has a rich 1D structure in 2D/3D data. Scalable #DeepLearning for #LArTPC! https://t.co/mbXCxjEgJC
arxiv_cscv: Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Data https://t.co/dOCwQNlDyC
arxiv_cscv: Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Data https://t.co/dOCwQN42H4
arxiv_cscv: Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Data https://t.co/dOCwQN42H4
arxiv_cscv: Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Data https://t.co/dOCwQN42H4
arxiv_cscv: Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Data https://t.co/dOCwQN42H4
arxiv_cscv: Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Data https://t.co/dOCwQN42H4
arxiv_cscv: Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Data https://t.co/dOCwQN42H4
arxivml: "Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Data", … https://t.co/vWVH3gm0Tn
arxiv_cscv: Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Data https://t.co/dOCwQN42H4
ComputerPapers: Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Data. https://t.co/Ebqcjr48qz
Images
Related