Publicación:
Vertex reconstruction of neutrino interactions using deep learning
Vertex reconstruction of neutrino interactions using deep learning
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Fecha
2017
Autores
Terwilliger A.M.
Perdue G.N.
Isele D.
Patton R.M.
Young S.R.
Título de la revista
Revista ISSN
Título del volumen
Editor
Institute of Electrical and Electronics Engineers Inc.
Proyectos de investigación
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Abstracto
Deep learning offers new tools to improve our
understanding of many important scientific problems. Neutrinos
are the most abundant particles in existence and are hypothesized
to explain the matter-antimatter asymmetry that dominates our
universe. Definitive tests of this conjecture require a detailed
understanding of neutrino interactions with a variety of nuclei.
Many measurements of interest depend on vertex reconstruction
– finding the origin of a neutrino interaction using data from
the detector, which can be represented as images. Traditionally,
this has been accomplished by utilizing methods that identify
the tracks coming from the interaction. However, these methods
are not ideal for interactions where an abundance of tracks and
cascades occlude the vertex region. Manual algorithm engineering
to handle these challenges is complicated and error prone. Deep
learning extracts rich, semantic features directly from raw data,
making it a promising solution to this problem. In this work, deep
learning models are presented that classify the vertex location
in regions meaningful to the domain scientists improving their
ability to explore more complex interactions.
Descripción
Palabras clave
Vertex reconstruction,
Elementary particles,
Neutrons,
Semantics,
Algorithm engineering,
Error prones,
Learning models,
Measurements of,
Neutrino interactions,
Semantic features,
Vertex locations,
Deep learning