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Learning New Physics from a machine
<!--HTML-->We propose using neural networks to detect data departures from a given reference model, with no prior bias on the nature of the new physics responsible for the discrepancy. The model-independent nature of our approach, and its ability to deal with rare signals such as those expecte...
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| Lenguaje: | eng |
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2018
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| Acceso en línea: | http://cds.cern.ch/record/2644193 |