  We develop a machine learning method for mapping data originating from both
Standard Model processes and various theories beyond the Standard Model into a
unified representation (latent) space while conserving information about the
relationship between the underlying theories. We apply our method to three
examples of new physics at the LHC of increasing complexity, showing that
models can be clustered according to their LHC phenomenology: different models
are mapped to distinct regions in latent space, while indistinguishable models
are mapped to the same region. This opens interesting new avenues on several
fronts, such as model discrimination, selection of representative benchmark
scenarios, and identifying gaps in the coverage of model space.
