SCYNet: testing supersymmetric models at the LHC with neural networks

SCYNet: testing supersymmetric models at the LHC with neural networks

Abstract SCYNet (SUSY Calculating Yield Net) is a tool for testing supersymmetric models against LHC data. It uses neural network regression for a fast evaluation of the profile likelihood ratio. Two neural network approaches have been developed: one network has been trained using the parameters of...

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Bibliographic Details
Main Authors: Philip Bechtle, Sebastian Belkner, Daniel Dercks, Matthias Hamer, Tim Keller, Michael Krämer, Björn Sarrazin, Jan Schütte-Engel, Jamie Tattersall
Format: Article
Language:English
Published: SpringerOpen 2017-10-01
Series:European Physical Journal C: Particles and Fields
Online Access:http://link.springer.com/article/10.1140/epjc/s10052-017-5224-8

Internet

http://link.springer.com/article/10.1140/epjc/s10052-017-5224-8

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