Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE
Name
PhysRevD.103.052012.pdf
Description
Published version
Size
3.63 MB
Format
Adobe PDF
Checksum (MD5)
5ddbbc7d3ea7b0d4ac3b577fbf749429
Author(s)
Conrad, Janet
Hen, Or
Date Issued
2021
Journal
Physical Review D
Publisher
American Physical Society (APS)
Citation
Conrad, Janet and Hen, Or. 2021. "Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE." Physical Review D, 103 (5).
Version
Final published version
Abstract
We present the performance of a semantic segmentation network, SparseSSNet,
that provides pixel-level classification of MicroBooNE data. The MicroBooNE
experiment employs a liquid argon time projection chamber for the study of
neutrino properties and interactions. SparseSSNet is a submanifold sparse
convolutional neural network, which provides the initial machine learning based
algorithm utilized in one of MicroBooNE's $\nu_e$-appearance oscillation
analyses. The network is trained to categorize pixels into five classes, which
are re-classified into two classes more relevant to the current analysis. The
output of SparseSSNet is a key input in further analysis steps. This technique,
used for the first time in liquid argon time projection chambers data and is an
improvement compared to a previously used convolutional neural network, both in
accuracy and computing resource utilization. The accuracy achieved on the test
sample is $\geq 99\%$. For full neutrino interaction simulations, the time for
processing one image is $\approx$ 0.5 sec, the memory usage is at 1 GB level,
which allows utilization of most typical CPU worker machine.
MIT Department
Massachusetts Institute of Technology. Department of Physics
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DOI of Published Version
https://doi.org/10.1103/PHYSREVD.103.052012
