SModelS v1.0: a short user guide
Sabine Kraml, Suchita Kulkarni, Ursula Laa, Andre Lessa, Veronika Magerl, Wolfgang Magerl, Doris Proschofsky-Spindler, Michael Traub, Wolfgang Waltenberger
LLPSC14295HEPHY-PUB 945/14
SModelS v1.0: a short user guide
Sabine Kraml , Suchita Kulkarni , , Ursula Laa , , Andre Lessa ,Veronika Magerl , Wolfgang Magerl , Doris Proschofsky-Spindler * ,Michael Traub , Wolfgang Waltenberger Laboratoire de Physique Subatomique et de Cosmologie, Université Grenoble-Alpes, CNRS/IN2P3,53 Avenue des Martyrs, F-38026 Grenoble, France Institut für Hochenergiephysik, Österreichische Akademie der Wissenschaften,Nikolsdorfer Gasse 18, 1050 Wien, Austria Instituto de Física, Universidade de São Paulo, São Paulo - SP, Brazil
Email: [email protected]
Abstract
SModelS is a tool for the automatic interpretation of simplified-model results from the LHC.Version 1.0 of the code is now publicly available. This document provides a quick user guide forinstalling and running SModelS v1.0. * Present address: Leopoldsgasse 21/2/11, 1020 Wien a r X i v : . [ h e p - ph ] D ec Introduction
SModelS [1] is an automatised tool for interpreting simplified-model results from the LHC. It isbased on a general procedure to decompose Beyond the Standard Model (BSM) collider signaturespresenting a Z symmetry into Simplified Model Spectrum (SMS) topologies. Our concrete imple-mentation currently focusses on supersymmetry (SUSY) searches with missing transverse energy, forwhich a large variety of SMS results from ATLAS and CMS are available. The main ingredients ofSModelS are ∙ the decomposition of the BSM spectrum into SMS topologies, ∙ the database of experimental SMS results, ∙ the interface between decomposition and the results database.The working principle is illustrated schematically in Fig. 1.Figure 1: Schematic view of the working principle of SModelS .Version 1.0 of SModelS is now publicly available. This document provides a short user guidefor the installation and for running SModelS v1.0. More details can be found in the primary physicspublication [1] and in the documentation provided with the code (an html manual, which is alsoavailable online at [2]). In case of problems using SModelS v1.0, we kindly ask the user to contactthe authors at [email protected]. 1
Download and Installation
SModelS is a Python library that requires Python version 2.6 or later (but not version 3). Inter-nally, SModelS v1.0 uses Pythia 6.4.27 [3], NLL-fast versions 1.2 and 2.1 [4] (see also [5–11]) anda modified version of PySLHA [12]. It has been tested on Ubuntu 14.X, Scientific Linux (CERN) 5and 6, as well as on Mac Os X 10.9 and 10.10.The SModelS v1.0 package can be downloaded from [2]. Unpacking the tar ball with tar -zxvf smodels-v1.0.tgz creates the directory smodels-v1.0 , where the code (subdirectory smodels ) and the resultsdatabase (subdirectory smodels-database ) are located.For installation, SModelS makes use of Python’s setuptools. On most machines (apart from SLand SLC) python setup.py install inside the smodels-v1.0 directory should install the entire project, resolving automatically the ex-ternal Python dependencies and compiling the internal Pythia 6 and NLL-fast versions using gfortran .Note that depending on the path specified in setup.py you may need to do the installation as supe-ruser.More details on the installation procedure, external dependencies and in particular instructions forinstallation on Scientific Linux can be found in the README file in smodels-v1.0 . When you use results obtained with SModelS v1.0 in a publication, please cite this documentas well as the original SModelS paper [1], Pythia 6.4 [3], NLL-fast [5–11], and PySLHA [12]. Forconvenience, these citations are provided in bibtex format in the SModelS v1.0 distribution.
SModelS v1.0 can be used with two forms of inputs ∙ SLHA (SUSY Les Houches Accord) [13] files containing masses,branching ratios and cross-sections, or ∙ LHE (Les Houches Event) [14] files containing parton-level events.The SLHA format is usually more compact and best suited for supersymmetric models. On theother hand, a LHE file can always be generated for any BSM model (through the use of your favorite2onte Carlo generator). In this case, however, the precision of the results is limited by the MCstatistics used to generate the file.In the case of
SLHA input , the production cross sections for the BSM states also have to beincluded as SLHA blocks, according to the SLHA cross-section format [15]. For the MSSM andsome of its extensions, the cross sections can be conveniently calculated and added to the SLHAinput file by means of the internal SModelS’ xseccomputer described in the tools section of thehtml manual.In the case of
LHE input , the total production cross-section as well as the center-of-mass energyshould be listed in the
Some comments are in order. ∙ The INPUTFILE can be an SLHA or LHE file as explained in Section 4.1. ∙ The PARAMETERFILE controls the basic options and parameters used by runSModelS.py .An example including all available parameters together with a short description, is providedas parameters.ini . If no parameter file is specified, the default parameters stored in etc/parameters_default.ini are used. Note that the input type (SLHA or LHE)needs to be properly specified in the parameter file. ∙ If no OUTPUTFILE is specified, the file output will be printed to summary.txt . SModelS v1.0 can easily be used for non-SUSY models as long as they present a Z -type symmetry. However, itis the responsibility of the user to make sure that the SMS results in the database actually apply to the model underconsideration. In this context, see also the caveats in [1]. .3 Default output The results of runSModelS.py are printed to the screen and to the output (summary) file. Thelevel of detail is controlled via the parameters file. The screen output comprises ∙ a full list of the topologies generated by the decomposition procedure(if printDecomp = True ), ∙ a list of all the analyses considered (if printAnalyses = True ), ∙ a list of all the theory predictions obtained and the corresponding upper limits from the experi-ments (if printResults = True ), ∙ possible warnings or error messages.The file output contains status flags for the input file and the decomposition, indicating possibleproblems. The status flags are followed by the name of the input file, basic information on the runparameters, and the version of the database used. This looks as follows: Input status: 1Decomposition output status: 1
The main part of the output file is then the list of analyses which constrain the input model.For each analysis, its ID, Tx name, centre-of-mass energy and the amount of condition violationare given, followed by the predicted signal cross section and the 95% CL experimental upper limiton it. The last entry in the line is the ratio r of the signal cross-section and the upper limit, r = σ ( predicted ) / σ ( excluded ) , where σ effectively means σ × B or the weight of the topology. A valueof r ≥ In this example, the topologies tested by the analysis are shown in bracket notation just below the anal-ysis entry, here e.g. [[[’jet’]],[[’jet’]]] . This can be turned off by setting addConstraintInfo= False in the parameter file. The last line of this block reports the maximum value of r : The highest r value is 4.71E-01 The
Tx names are explained in the SMS dictionary on http://smodels.hephy.at/wiki/SmsDictionary. expandedSummary=False ; in this caseonly the most constraining analysis (the one with the maximum r value) is printed.Finally, if findMissingTopos = True , a list of the missing topologies (in bracket notation)and their cross sections at the given √ s is also included. This list is ordered from high to low crosssections; per default only the 10 leading ones are printed. An example is shown below: Missing topologies with the highest cross-sections (up to 10):Sqrts (TeV) Weight (fb) Element description8.00E+00 1.567E+01
For more details, including an explanation of the bracket notation, we refer the user to the htmlmanual.
Although runSModelS.py provides the main SModelS features with a command line interface,users familiar with Python and the SModelS language may prefer to write their own main program,using SModelS v1.0 as a Python library. A simple example code for this purpose is provided as
Example.py in the SModelS v1.0 distribution. Further examples are presented in the “More Exam-ples” section of the manual.
The SModelS v1.0 database comprises a large number of SMS results from ATLAS and CMSSUSY searches at √ s = ID short description L Ref. Tx names
ATLAS-SUSY-2013-02 0 leptons + 2–6 jets + ̸ E T ≥ ̸ E T ̸ E T e , µ ) + ̸ E T e , µ , τ ) + ̸ E T ̸ E T ̸ E T ̸ E T ≥ ̸ E T ̸ E T ̸ E T ≥ ̸ E T ̸ E T The luminosity L is given in [fb − ]; the shorthand notation T1tttt(off) stands for “T1tttt and T1ttttoff” (on-shell andoff-shell, respectively). Likewise TChiWZ(off) denotes “TChiWZ and TChiWZoff”. D short description L Ref. Tx names
CMS-SUS-12-024 0 leptons + ≥ ̸ E T ̸ E T , α T ≥ ̸ E T ≥ ̸ E T ≥ ̸ E T ̸ H T ̸ E T ̸ E T ≥ ̸ E T ̸ E T , M CT M T2 runSModelS.py . They can be activated by smsAnalysisFactory.load(useSuperseded=True) when using SModelS v1.0 as a Python library. (For more details, see "How to load the database" inthe “More Examples” section of the html manual.) Such superseded results can be useful, e.g. , forcomparing with older studies. It is, however, strongly discouraged to use them in any other case.Last but not least we note that for topologies with more than one step in the decay chain, e.g. charginos decaying through intermediate sleptons, or stops decaying into bottom plus chargino fol-lowed by the chargino decay into the lightest neutralino, we need several (more than one) mass planesin order to interpolate between them. Whenever only one mass plane is provided, the result is notuseful for our purpose and thus not included in the database.
We presented the first public release of SModelS, an automatic tool for interpreting simplified-model results from the LHC in generic models possessing a Z symmetry. SModelS v1.0 consistsof a SMS decomposition procedure, a database of SMS cross section upper limits from ATLAS andCMS, and an interface between these two components to confront the theoretical predictions of BSMmodels with the experimental results. The database of v1.0 comprises results from 13 ATLAS and 13CMS SUSY searches at 8 TeV, corresponding to 21 ATLAS and 41 CMS results when counting theindividual results (Tx names) in the different publications. In this document, we provided the basic6nstructions for installing and running SModelS v1.0 and understanding its output. More detailedexplanations are given in the html manual which comes as part of the SModelS v1.0 distribution.We hope that SModelS v1.0 will be a useful tool for the High-Energy Physics community andcontribute to the legacy of the LHC results. Here note that SModelS v1.0 can be used not only to testwhether a particular scenario is excluded by the recent LHC results — it can also be convenientlyused to classify untested regions, missing topologies and new signatures that might be interesting tolook for.This said, the release of SModelS v1.0 is but the beginning of the story. Several extensions andimprovements are already in development. One such extension will be a more extensive database ofexperimental results, ideally comprising also non-SUSY searches. This, however, depends also onthe co-operation of the ATLAS and CMS search groups in making the relevant information available.Another extension will be the inclusion of efficiency maps. This will be a major upgrade of the code.Further plans include ways to determine the most sensitive analysis and ways to combine resultsfrom different analyses, extensions to asymmetric branches (if suitable experimental results becomeavailable for this) and signatures without missing energy. Acknowledgements
We thank the ATLAS and CMS SUSY groups for helpful discussions on their results, and inparticular for providing (most of) the SMS cross section upper limits used here in digital format.This work is supported in part by the French ANR project DMA
STRO
LHC. Su.K. is supportedby the “New Frontiers” program of the Austrian Academy of Sciences. U.L. is supported by the“Investissements d’avenir, Labex ENIGMASS”. A.L. is supported by FAPESP; he acknowledgesmoreover the hospitality of LPSC Grenoble and of HEPHY Vienna. V.M. is grateful for financialsupport by the FEMtech initiative of the BMVIT of Austria.
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