  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/.
