Details

Autor(en) / Beteiligte

• Ambrogi, Federico
• Dutta, Juhi
• Heisig, Jan
• Kraml, Sabine
• Kulkarni, Suchita
• Laa, Ursula
• Lessa, Andre
• Neuhuber, Philipp
• Reyes-González, Humberto
• Waltenberger, Wolfgang
• Wolf, Matthias

Titel

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

Ist Teil von

• Computer physics communications, 2020-06, Vol.251, p.106848, Article 106848

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• SModelS is an automatized tool enabling the fast interpretation of simplified model results from the LHC within any model of new physics respecting a Z2 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 Z2-odd particle and appropriately takes into account missing energy, heavy stable charged 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. Program Title: SModelS Program Files doi:http://dx.doi.org/10.17632/w4nft4459w.2 Licensing provisions: GPLv3 Programming language: Python3 Journal reference of previous version: Comput. Phys. Commun. 227 (2018) 72 Does the new version supersede the previous version?: Yes Reasons for the new version: Addition of new features. 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 charged 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 the results of a model scan. Finally, the whole code now also runs with Python3, 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. Additional comments including restrictions and unusual features: At present, only models with a Z2-like symmetry can be tested. Each SMS is defined purely by the vertex structure and the final-state particles; initial and intermediate BSM particles are described only by their masses, production cross sections, branching ratios and total widths. 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. [1] F. Ambrogi et al., “SModelS v1.1 user manual: Improving simplified model constraints with efficiency maps,” Comput. Phys. Commun. 227 (2018) 72 [arXiv:1701.06586 [hep-ph]].