CheckMATE 2: From the model to the limit

Abstract

We present the latest developments to the CheckMATE program that allows models of new physics to be easily tested against the recent LHC data. To achieve this goal, the core of CheckMATE now contains over 60 LHC analyses of which 12 are from the 13 TeV run. The main new feature is that CheckMATE 2 now integrates the Monte Carlo event generation via MadGraph5_aMC@NLO and Pythia 8. This allows users to go directly from a SLHA file or UFO model to the result of whether a model is allowed or not. In addition, the integration of the event generation leads to a significant increase in the speed of the program. Many other improvements have also been made, including the possibility to now combine signal regions to give a total likelihood for a model. Program summary Program Title: CheckMATE Program Files doi:http://dx.doLorg/10.17632/k4pnk5wrfm.1 Licensing provisions: GPLv3 Programming language: C++, Python External routines/libraries: ROOT, Python, HepMC (optional) Pythia 8 (optional), Madgraph5_aMC@NLO (optional) Subprograms used: Delphes Nature of problem: The LHC experiments have performed a huge number of searches for new physics in the past few years. However the results can only be given for a few benchmark models out of the huge number that exist in the literature. Solution method: CheckMATE is a program that automatically calculates limits for new physics models. The original version required the user to generate Monte Carlo events themselves before CheckMATE could be run but the new version now integrates this step. The simplest output of CheckMATE is whether the model is ruled out at 95% CLs or not. However, more complicated statistical metrics are also available, including the combination of many signal regions. Restrictions: Only a subset of available experimental results have been implemented. Additional comments: CheckMATE is built upon the tools and hard work of many people. If CheckMATE is used in your publication it is extremely important that all of the following citations are included, - Delphes 3 [1]. https://cp3.irmp.ucl.ac.be/projects/delphes - FastJet [2,3]. http://fastjet.fr/ - Anti-k(t) jet algorithm [4]. - CLs prescription [5]. - All experimental analyses that were used to set limits in the study and if the analysis was implemented by non-CheckMATE authors, the relevant implementation reference. - MadGraph5_aMC@NLO [6] if it is used to calculate the hard matrix element from within CheckMATE. https://launchpad.net/mg5amcnlo - Pythia 8.2 [7] if showering or matching is done from within CheckMATE. http://home.thep.lu.se/-torbjorn/Pythia.html - The Monte Carlo event generator that was used if. hepmc or. The files were generated externally. - In analyses that use the m(T2) kinematical discriminant [8,9] we use the mt2_bisect library [10]. We also include the M-T2(bl) and M-T2(W) derivatives [11]. http://particle.physics.ucdavis.edu/hefti/projects/doku.php?id=wimpmass https://sites.google.com/a/ucdavis.edu/mass/ - In analyses that use the M-cr family of kinematical discriminants we use the MctLib library that includes the following variables, Ma [12], M-cr corrected [13], Ma parallel and perpendicular [14]. https://mctlib.hepforge.org/ - In analyses that use topness variable we use the topness library [15]. https://github.com/michaelgraesser/topness - Super-Razor [16] in analyses that use this variable. [1]). de Favereau et al. [DELPHES 3 Collaboration], JHEP 1402 (2014) 057 [arXiv:1307.6346 [hep-ex]]. [2] M. Cacciari, G. P. Salam and G. Soyez, Eur. Phys.). C 72 (2012) 1896 [arXiv:1111.6097 [hep-ph]]. [3] M. Cacciari and G. P. Salam, Phys. Lett. B 641(2006) 57 [hep-ph/0512210]. [4] M. Cacciari, G. P. Salam and G. Soyez, JHEP 0804 (2008) 063 [arXiv:0802.1189 [hep-ph]]. [5] A. L. Read, J. Phys. G 28 (2002) 2693. [6] J. Alwall et al., JHEP 1407 (2014) 079 [arXiv:1405.0301 [hep-ph]]. [7] T. Sjostrand et al., Comput. Phys. Commun. 191 (2015) 159 [arXiv:1410.3012 [hep-ph]]. [8] C. G. Lester and D. J. Summers, Phys. Let