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Learning New Physics from a Machine
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 virtues of neural networks as unbiased function approximants make them particularly suited for this task. An algorithm tha...
| Autores principales: | , |
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| Lenguaje: | eng |
| Publicado: |
2018
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1103/PhysRevD.99.015014 http://cds.cern.ch/record/2627052 |
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