The BSM-AI project: SUSY-AI - Generalizing LHC limits on Supersymmetry   with Machine Learning
  A key research question at the Large Hadron Collider (LHC) is the test of models of new physics. Testing if a particular parameter set of such a model is excluded by LHC data is a challenge: It requires the time consuming generation of scattering events, the simulation of the detector response, the event reconstruction, cross section calculations and analysis code to test against several hundred signal regions defined by the ATLAS and CMS experiment. In the BSM-AI project we attack this challenge with a new approach. Machine learning tools are thought to predict within a fraction of a millisecond if a model is excluded or not directly from the model parameters. A first example is SUSY-AI, trained on the phenomenological supersymmetric standard model (pMSSM). About 300,000 pMSSM model sets - each tested with 200 signal regions by ATLAS - have been used to train and validate SUSY-AI. The code is currently able to reproduce the ATLAS exclusion regions in 19 dimensions with an accuracy of at least 93 percent. It has been validated further within the constrained MSSM and a minimal natural supersymmetric model, again showing high accuracy. SUSY-AI and its future BSM derivatives will help to solve the problem of recasting LHC results for any model of new physics.   SUSY-AI can be downloaded at http://susyai.hepforge.org/. An on-line interface to the program for quick testing purposes can be found at http://www.susy-ai.org/. 
Sascha Caron, Jong Soo Kim, Krzysztof Rolbiecki, Roberto Ruiz de   Austri and Bob Stienen
hep-ph hep-ex