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Register a new userSCYNet: Testing supersymmetric models at the LHC with neural networks
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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. 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.
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The search for heavy Higgs bosons is an essential step in the exploration of the Higgs sector and in probing the Supersymmetric parameter space. This paper discusses the constraints on the M(A) and tan beta parameters derived from the bounds on the different decay channels of the neutral H and A bosons accessible at the LHC, in the framework of the phenomenological MSSM. The implications from the present LHC results and the expected sensitivity of the 14 TeV data are discussed in terms of the coverage of the [M(A) - tan beta] plane. New channels becoming important at 13 and 14 TeV for low values of tan beta are characterised in terms of their kinematics and the reconstruction strategies. The effect of QCD systematics, SUSY loop effects and decays into pairs of SUSY particles on these constraints are discussed in details.
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