- Typ
- Artikel in einem Journal
- Gebiet
- Anderes Gebiet
- Autor
- Philip Bechtle, Sebastian Belkner, Daniel Dercks, Matthias Hamer, Tim Keller, Michael KrÃ¤mer, BjÃ¶rn Sarrazin, Jan SchÃ¼tte-Engel, Jamie Tattersall, …, P. Bechtle, M. Hamer (für die Fittino/SCYNet-Kollaboration)
- Titel
- SCYNet: Testing supersymmetric models at the LHC with neural networks
- Datum
- 2017-03
- Reportnummer
- —
- Kurzfassung
- 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. While the calculation of the energies and particle multiplicities takes up computation time, the corresponding neural network is more general and can be used to predict the LHC profile likelihood ratio for a wider class of new physics models.
- Link
- https://arxiv.org/abs/1703.01309