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Accelerating high-energy physics exploration with deep learning
In this work, we present our approach to using deep learning for identification of rarely produced physics particles (such as the Higgs Boson) out of a majority of uninteresting, background or noise-dominated data. A fast and efficient system to eliminate uninteresting data would result in much less...
| Autores principales: | , , , , |
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| Lenguaje: | eng |
| Publicado: |
2017
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1145/3093338.3093340 http://cds.cern.ch/record/2320258 |