Speaker
Description
In supersymmetric theories, the Higgs boson masses are calculated from the input parameters. Moreover, to be compatible with phenomenology, higher-order corrections have to be included in their derivation. Experimental analyses on the other hand resort to benchmarks with specific mass values. Given the large number of input parameters in the proposed supersymmetric extension, such as the NMSSM, the exploration of the parameter space, compatible with all existing constraints as well as with additional requests on the desired parameter sets, becomes difficult. We present the program package NMSSMScanner, based on the code NMSSMCALC, that applies machine learning techniques to efficiently consider all the relevant theoretical and experimental constraints. We provide sample benchmarks on the production of a pair of Higgs bosons, a SM-like plus a non-SM-like one, in various final states and with mass combinations that can be tested by the experimental groups.
