SModelS v1.1 user manual: Improving simplified model constraints with efficiency maps

SModelS is an automatized tool for the interpretation of simplified model results from the LHC. It allows to decompose models of new physics obeying a Z(2) symmetry into simplified model components, and to compare these against a large database of experimental results. The first release of SModelS, v1.0, used only cross section upper limit maps provided by the experimental collaborations. In this new release, v1.1, we extend the functionality of SModelS to efficiency maps. This increases the constraining power of the software, as efficiency maps allow to combine contributions to the same signal region from different simplified models. Other new features of version 1.1 include likelihood and X-2 calculations, extended information on the topology coverage, an extended database of experimental results as well as major speed upgrades for both the code and the database. We describe in detail the concepts and procedures used in SModelS v1.1, explaining in particular how upper limits and efficiency map results are dealt with in parallel. Detailed instructions for code usage are also provided. Program summary Program Title: SModelS Program Files doi: http ://dx.doi.org/10.17632/w4nft4459w.1 Licensing provisions: GPLv3 Programming language: Python Nature of problem: The results for searches for new physics beyond the Standard Model (BSM) at the Large Hadron Collider are often communicated by the experimental collaborations in terms of constraints on so-called simplified models spectra (SMS). Understanding how SMS constraints impact a realistic new physics model, where possibly a multitude of relevant production channels and decay modes are relevant, is a non-trivial task. Solution method: We exploit the notion of simplified models to constrain full models by decomposing them into their SMS components. A database of SMS results obtained from the official results of the ATLAS and CMS collaborations, but in part also from 'recasting' the experimental analyses, can be matched against the decomposed model, resulting in a statement to what extent the model at hand is in agreement or contradiction with the experimental results. Further useful information on, e.g., the coverage of the models' signatures is also provided. Additional comments including Restrictions and Unusual features: At present, the tool is limited to signatures with missing transverse energy, and only models with a Z(2)-like symmetry can be tested. Each SMS is defined purely by the vertex structure and the SM final state particles; BSM particles are described only by their masses, production cross sections and branching ratios. Possible differences in signal selection efficiencies arising, e.g., from different production mechanisms or from the spin of the BSM particles, are ignored in this approach. Since only part of the full model can be constrained by SMS results, SModelS will always remain more conservative (though orders of magnitude faster) than full recasting approaches. (C) 2018 Elsevier B.V. All rights reserved.