SCYNet: testing supersymmetric models at the LHC with neural networks

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 the 11-di...

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Veröffentlicht in:The European physical journal. C, Particles and fields Particles and fields, 2017-10, Vol.77 (10), p.1-20, Article 707
Hauptverfasser: Bechtle, Philip, Belkner, Sebastian, Dercks, Daniel, Hamer, Matthias, Keller, Tim, Krämer, Michael, Sarrazin, Björn, Schütte-Engel, Jan, Tattersall, Jamie
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Sprache:eng
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Zusammenfassung: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 the 11-dimensional phenomenological Minimal Supersymmetric Standard Model (pMSSM-11) as an input and evaluates the corresponding profile likelihood ratio within milliseconds. It can thus be used in global pMSSM-11 fits without time penalty. In the second approach, the neural network has been trained using model-independent signature-related objects, such as energies and particle multiplicities, which were estimated from the parameters of a given new physics model.
ISSN:1434-6044
1434-6052
DOI:10.1140/epjc/s10052-017-5224-8