SModelS v1.2: Long-lived particles, combination of signal regions, and other novelties

Abstract

SModelS is an automatised tool enabling the fast interpretation of simplified model results from the LHC within any model of new physics respecting a Z 2 symmetry. With the version 1.2 we announce several new features. First, previous versions were restricted to missing energy signatures and assumed prompt decays within each decay chain. SModelSv1.2 considers the lifetime of each Z 2 -odd particle and appropriately takes into account missing energy, heavy stable charge particle and R-hadron signatures. Second, the current version allows for a combination of signal regions in efficiency map results whenever a covariance matrix is available from the experiment. This is an important step towards fully exploiting the constraining power of efficiency map results. Several other improvements increase the user-friendliness, such as the use of wildcards in the selection of experimental results, and a faster database which can be given as a URL. Finally, smodelsTools provides an interactive plots maker to conveniently visualize the results of a model scan. Summary of revisions:The most important new features in v1.2 are the combination of signal regions in efficiency map results whenever a covariance matrix is available from the experiment, and the implementation of heavy stable charge particle and R-hadron signatures. Moreover, the database of experimental results can now be given as a URL, and the pickling has been improved to make the database faster. Other improvements include that wildcards are allowed when selecting analyses, datasets or topologies, and that the path to the model file, formerly required to be smodels/sparticles.py, can be specified in the parameters card. For the convenience of the user, we also provide a tool to make interactive plots to visualize results of a model scan. Finally, the whole code now also runs with Python 3, which has become the recommended default, and it can now be installed in its source directory.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 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 model's signatures is also provided.

Reinterpretation of LHC Results for New Physics: Status and recommendations after Run 2

“…This information allows for the reconstruction of an approximate likelihood for the given simplified models and a more precise calculation of the analysis sensitivity for new models. Both upper limits and efficiencies (including correlation matrices) have been successfully used to reinterpret the experimental results, as has been demonstrated by the SModelS [33,35,36] program and its applications. It would be beneficial if simplified model efficiency maps were systematically provided for all signal regions, together with a (simplified or full) likelihood model for combining them.…”

“…All one then needs to compute is signal weights in terms of cross-sections × branching ratios (× efficiencies). There is one current public tool, SModelS [33,35,36], which applies this approach generically to BSM models. Clearly, this is easier and much faster than event simulation and additionally it allows for reinterpreting searches which are not cut and count, e.g.…”

“…The approach taken by SModelS [33,35,36] does not employ MC event generation. 7 Instead, a given BSM model is decomposed into simplified model spectrum (SMS) components, whose weights (σ×BRs) are then matched against a database of LHC results.…”

“…The benefit of this approach lies in its speed; it takes only a few seconds to confront a given theory with the entire database of results. Another advantage is that prompt and long-lived searches can be treated in essentially the same way [36]. These advantages come at the price of being conservative: because only a part of all occurring signatures is constrained by corresponding SMS results in the database, the resulting limits will always be less constraining than the ones obtained from fully recasting a given analysis.…”

Constraining electroweakinos in the minimal Dirac gaugino model

“…SModelS [102][103][104][105]. [90], which moreover automatically produces the correct particles.py file to declare the even and odd particle content for SModelS.…”