SCYNET. Parametrizing the LHC search results for SUSY using a Neural Net regression

The LHC has already excluded many signatures of New Physics based on searches for various topologies. Each of the individual searches for different topologies measures a background expectation and a measured number of events along with statistical and systematical uncertainties. These published results can be used to set limits on new models of New Physics. A possible tool for such a study is e.g. CheckMATE. For each model it tests against the LHC results, it generates events, uses a fast detector simulation, performs the selection, and then compares the selected number of signal events to the background and data. This is a very general approach, however, it is very slow. In order to make this approach useful for global fits, the evaluation of each model point must take O(<1)s. In SCYNET, this is realized by training an Artificial Neural Net regression on O (800 k) simulated SUSY model points using CheckMATE for 8 TeV and 13 TeV LHC SUSY searches. In a direct approach, the parameters of the pMSSM11 are trained against a χ² characterizing the agreement of signal and background with the data in all independent searches. In the indirect approach, pseudo-observables such as the number of partons are used to parametrize the net, such that any model of New Physics and not only a specific SUSY model can be used.