Journal article
Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber
- Abstract:
- We have developed a convolutional neural network that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a νμ charged-current neutral pion data samples.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Version of record, pdf, 6.2MB, Terms of use)
-
- Publisher copy:
- 10.1103/PhysRevD.99.092001
Authors
- Publisher:
- American Physical Society
- Journal:
- Physical Review D More from this journal
- Volume:
- 99
- Issue:
- 9
- Article number:
- 092001
- Publication date:
- 2019-05-07
- DOI:
- EISSN:
-
2470-0029
- ISSN:
-
2470-0010
- Keywords:
- Pubs id:
-
pubs:910060
- UUID:
-
uuid:c57cd89d-1ace-4036-ab29-12f34cff23c7
- Local pid:
-
pubs:910060
- Source identifiers:
-
910060
- Deposit date:
-
2019-09-26
Terms of use
- Copyright holder:
- Adams et al
- Copyright date:
- 2019
- Notes:
- Copyright 2019 The Author(s). Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license.
- Licence:
- CC Attribution (CC BY)
If you are the owner of this record, you can report an update to it here: Report update to this record