Journal
EUROPEAN PHYSICAL JOURNAL C
Volume 77, Issue 10, Pages -Publisher
SPRINGER
DOI: 10.1140/epjc/s10052-017-5224-8
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Funding
- German Research Foundation (DFG) through the Forschergruppe New Physics at the Large Hadron Collider [FOR 2239]
- Helmholtz Alliance Physics at the Terascale
- BMBF-FSP 101
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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.
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