Journal article
Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber
- Abstract:
- We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 6.9MB, Terms of use)
-
- Publisher copy:
- 10.1088/1748-0221/12/03/P03011
Authors
- Publisher:
- Institute of Physics
- Journal:
- Journal of Instrumentation More from this journal
- Volume:
- 7
- Pages:
- P03011
- Publication date:
- 2017-03-01
- Acceptance date:
- 2017-02-28
- DOI:
- Keywords:
- Pubs id:
-
pubs:660294
- UUID:
-
uuid:baa86ba5-6404-48d1-9556-86ef3543c18a
- Local pid:
-
pubs:660294
- Source identifiers:
-
660294
- Deposit date:
-
2017-03-09
- ARK identifier:
Terms of use
- Copyright holder:
- IOP Publishing Ltd and Sissa Medialab srl
- Copyright date:
- 2017
- Notes:
- © 2017 IOP Publishing Ltd and Sissa Medialab srl This is the accepted manuscript version of the article. The final version is available online from IOP Publishing at: http://dx.doi.org/10.1088/1748-0221/12/03/P03011
If you are the owner of this record, you can report an update to it here: Report update to this record