Rights statement: © 2019 American Physical Society
Accepted author manuscript, 3.39 MB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
| Article number | 092001 |
|---|---|
| <mark>Journal publication date</mark> | 7/05/2019 |
| <mark>Journal</mark> | Physical Review D |
| Issue number | 9 |
| Volume | 99 |
| Number of pages | 20 |
| Publication Status | Published |
| <mark>Original language</mark> | English |
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 nu(mu) charged-current neutral pion data samples.