Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
Vertex finding in neutrino-nucleus interaction: a model architecture comparison
Ist Teil von
Journal of instrumentation, 2022-08, Vol.17 (8), p.T08013
Ort / Verlag
Bristol: IOP Publishing
Erscheinungsjahr
2022
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
Abstract
We compare different neural network architectures for
machine learning algorithms designed to identify the neutrino
interaction vertex position in the MINERvA detector. The
architectures developed and optimized by hand are compared with the
architectures developed in an automated way using the package
“Multi-node Evolutionary Neural Networks for Deep Learning”
(MENNDL), developed at Oak Ridge National Laboratory. While the
domain-expert hand-tuned network was the best performer, the
differences were negligible and the auto-generated networks
performed as well. There is always a trade-off between human, and
computer resources for network optimization and this work suggests
that automated optimization, assuming resources are available,
provides a compelling way to save significant expert time.