Artificial proto-modelling with simplified-model results from the LHC.

We present a novel approach to identify potential dispersed signals of new physics in the slew of published LHC results. It employs a random walk algorithm to introduce sets of new particles, dubbed "proto-models", which are tested against simplified-model results from ATLAS and CMS searches for new physics by exploiting the SModelS software framework. A combinatorial algorithm identifies the set of analyses and/or signal regions that maximally violates the Standard Model hypothesis, while remaining compatible with the entirety of LHC constraints in our database. Crucial to the method is the ability to construct a reliable likelihood in proto-model space; we explain the various approximations which are needed depending on the information available from the experiments, and how they impact the whole procedure.