Search for supersymmetry in final states with photons and missing transverse momentum in proton-proton collisions at 13 TeV

Abstract

Results are reported for a search for supersymmetry in final states with photons and missing transverse momentum in proton-proton collisions at the LHC. The data sample corresponds to an integrated luminosity of 35.9 fb${}^{-1}$ collected at a center-of-mass energy of 13 TeV using the CMS detector. The results are interpreted in the context of models of gauge-mediated supersymmetry breaking. Production cross section limits are set on gluino and squark pair production in this framework. Gluino masses below 1.86 TeV and squark masses below 1.59 TeV are excluded at 95% confidence level.

Replaced with the published version. Added the journal reference and the DOI. All the figures and tables can be found at http://cms-results.web.cern.ch/cms-results/public-results/publications/SUS-17-011 (CMS Public Pages)

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• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• Particle-flow reconstruction and global event description with the CMS detector
• The CMS trigger system
• General Gauge Mediation
• Threshold resummation for squark-antisquark and gluino-pair production at the LHC
• Simplified Models for a First Characterization of New Physics at the LHC
• Soft gluon resummation for the production of gluino-gluino and squark-antisquark pairs at the LHC
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• Model-Independent Jets plus Missing Energy Searches
• Implementing General Gauge Mediation
• General Messenger Gauge Mediation

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• The effective field theory of low scale see-saw at colliders
• Strength in numbers: optimal and scalable combination of LHC new-physics searches
• Searching for light long-lived neutralinos at Super-Kamiokande
• Re-examining $N_R$-EFT Upto Dimension Six
• New Physics Search at the CEPC: a General Perspective
• Gravitational waves from first-order electroweak phase transition in a model with light sgoldstinos
• Shedding Light on Dark Matter at the LHC with Machine Learning