A tool for interpreting simplified-model results from the LHC

Gaël Alguero, Jan Heisig, Sabine Kraml, Andre Lessa, Timothée Pascal, Humberto Reyes-González, Wolfgang Waltenberger

Previously involved in SModelS: Federico Ambrogi, Charanjit K. Khosa, Juhi Dutta, Suchita Kulkarni, Ursula Laa, Veronika Magerl, Wolfgang Magerl, Philipp Neuhuber, Doris Proschofsky, Jory Sonneveld, Michael Traub, Matthias Wolf, Alicia Wongel

7 Apr 2022: SModelS version 2.2.0.post1 available

contains a small fix for python <= 3.7

31 Mar 2022: SModelS version 2.2.0 available (what’s new)

we thank Jamie Yellen (University of Glasgow) for beta testing, and a bug fix in the analysis combination code

19 Oct 2021: SModelS version 2.1.1 is now available (what’s new)

Accompanying physics paper: arXiv:2112.00769

A detailed documentation is available in the online manual
For instructions on how to install SModelS, check the installation section in the manual.
This tutorial is available from the latest Reinterpetation workshop.
You may also want to check the release notes and known issues
Here are the list of analyses in the latest database version, the respective validation plots and an SMS dictionary explaining the Tx names used by SModelS.
A discussion of re-interpretation methods and tools, and recommendations about the presentation of results can be found in this report by the LHC Reinterpretation Forum, arXiv:2003.07868.
SModelS support for pyhf likelihoods is described in arXiv:2009.01809 and the main novelties of SModelS v2 in arXiv:2112.00769.
An update of the micrOMEGAs interface for SModelS v2.2 is available on Zenodo

Mailing lists:

For questions and comments, send an e-mail to: smodels-users@lists.oeaw.ac.at.
To receive updates and announcements, subscribe to smodels-info.

If you use SModelS, please cite the following papers:

Constraining new physics with SModelS version 2, Gael Alguero, Jan Heisig, Charanjit Khosa, Sabine Kraml, Suchita Kulkarni, Andre Lessa, Humberto Reyes-Gonzalez, Wolfgang Waltenberger, Alicia Wongel, arXiv:2112.00769
A SModelS interface for pyhf likelihoods, Gael Alguero, Sabine Kraml, Wolfgang Waltenberger, arXiv:2009.01809, CPC March 2021, 107909
SModelS database update v1.2.3, Charanjit K. Khosa, Sabine Kraml, Andre Lessa, Philipp Neuhuber, Wolfgang Waltenberger, arXiv:2005.00555, LHEP 158 2020
SModelS v1.2: long-lived particles, combination of signal regions, and other novelties, Federico Ambrogi et al., arXiv:1811.10624, CPC 251, June 2020, 106848
Constraining new physics with searches for long-lived particles: Implementation into SModelS, Jan Heisig, Sabine Kraml, Andre Lessa, arXiv:1808.05229, Phys.Lett. B788 (2019) 87-95.
SModelS extension with the CMS supersymmetry search results from Run 2, Juhi Dutta, Sabine Kraml, Andre Lessa, Wolfgang Waltenberger, arXiv:1803.02204, LHEP 1 (2018) no.1,5-12
SModelS v1.1 user manual: improving simplified model constraints with efficiency maps, Federico Ambrogi, Sabine Kraml, Suchita Kulkarni, Ursula Laa, Andre Lessa, Veronika Magerl, Jory Sonneveld, Michael Traub, Wolfgang Waltenberger arXiv:1701.06586, CPC 227 (2018) 72-98
SModelS: a tool for interpreting simplified-model results from the LHC and its application to supersymmetry, Sabine Kraml, Suchita Kulkarni, Ursula Laa, Andre Lessa, Wolfgang Magerl, Doris Proschofsky, Wolfgang Waltenberger, arXiv:1312.4175, EPJC (2014) 74:2868

Moreover

If you use the cross section calculator please cite Pythia and NLLfast
If you use the Fastlim results in the database, please cite Fastlim 1.0 arXiv:1402.40492, EPJC74 (2014) 11.

For convenience a references.bib file containing all relevant references is provided with the code. Likewise, a database.bib file with references to all the ATLAS and CMS analyses used is provided in the text database. ————————————————————————

Working principle

SModelS is based on a general procedure to decompose Beyond the Standard Model (BSM) collider signatures presenting a Z₂ symmetry into Simplified Model Spectrum (SMS) topologies. Our method provides a way to cast BSM predictions for the LHC in a model independent framework, which can be directly confronted with the relevant experimental constraints. The main SModelS ingredients are

the decomposition of the BSM spectrum into SMS topologies
a database of experimental SMS results
the interface between decomposition and results database to compute limits

Code and Database updates

For code and database releases, see Download

Experimental results in the database

Here is the list of analyses contained in the latest database version
Same as above but including superseded analyses
Pretty validation plots for all analyses
We also provide an SMS dictionary explaining the Tx names used by SModelS

Publications and Talks

See the publications and talks page