The NMSSM is within Reach of the LHC: Mass Correlations & Decay Signatures

Abstract

The Next-to-Minimal Supersymmetric Standard Model (NMSSM), the singlet extension of the MSSM which fixes many of the MSSM's shortcomings, is shown to be within reach of the upcoming runs of the Large Hadron Collider (LHC). A systematic treatment of the various Higgs decay channels and their interplay has been lacking due to the seemingly large number of free parameters in the NMSSM's Higgs sector. We demonstrate that due to the SM-like nature of the observed Higgs boson, the NMSSM's Higgs and neutralino sectors have highly correlated masses and couplings and can effectively be described by four physically intuitive parameters: the physical masses of the two CP-odd states and their mixing angle, and $\tan \beta$, which plays a minor role. The heavy Higgs bosons in the NMSSM have large branching ratios into pairs of lighter Higgs bosons or a light Higgs and a $Z$ boson. Search channels arising via these Higgs cascades are unique to models like the NMSSM with a Higgs sector larger than that of the MSSM. In order to cover as much of the NMSSM parameter space as possible, one must combine conventional search strategies employing decays of the additional Higgs bosons into pairs of SM particles with Higgs cascade channels. We demonstrate that such a combination would allow a significant fraction of the viable NMSSM parameter space containing additional Higgs bosons with masses below 1 TeV to be probed at future runs of the LHC.

50 pages, 11 figures, 6 tables, 4 benchmark points. v2: matches the published version

References in corpus (10)

• Supersymmetry without a Light Higgs Boson
• NMSSM Interpretation of the Galactic Center Excess
• Complementarity Between Non-Standard Higgs Searches and Precision Higgs Measurements in the MSSM
• Discovery Prospects for NMSSM Higgs Bosons at the High-Energy Large Hadron Collider
• The Light and Heavy Higgs Interpretation of the MSSM
• Search for a 'stable alignment limit' in two Higgs-doublet models
• Challenges and opportunities for heavy scalar searches in the $t\bar{t}$ channel at the LHC
• Present Status and Future Tests of the Higgsino-Singlino Sector in the NMSSM
• Simultaneous Search for Extra Light and Heavy Higgs Bosons via Cascade Decays
• Light Higgsino Decays as a Probe of the NMSSM

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• Triple Higgs Boson Production at the Large Hadron Collider with Two Real Singlet Scalars
• HHH Whitepaper
• Singlino-dominated dark matter in general NMSSM
• Impact of recent $(g-2{)}_{\mu }$ measurement on the light CP-even Higgs scenario in general Next-to-Minimal Supersymmetric Standard Model
• Searches for Higgs boson production through decays of heavy resonances
• A relatively light, highly bino-like dark matter in the $Z_3$-symmetric NMSSM and recent LHC searches
• Searching for heavy Higgs in supersymmetric final states at the LHC
• Searching for the Higgsino-Bino Sector at the LHC
• Search for new particles in an extended Higgs sector with four b quarks in the final state at $\sqrt{s}=$ 13 TeV
• Impact of LHC Higgs couplings measurements on bosonic decays of the neutral Higgs sector in the scNMSSM
• The Scale of Superpartner Masses and Electroweakino Searches at the High-Luminosity LHC
• LHC signals of the next-to-lightest scalar Higgs state of the NMSSM in the $4\tau$ decay channel
• Search for new resonances decaying to pairs of merged diphotons in proton-proton collisions at $\sqrt{s}$ = 13 TeV
• Invisible dark matter decays of a non-Standard Model like CP-even scalar boson
• Fermionic decays of NMSSM Higgs bosons under LHC 13 TeV constraints
• Status of ${\mathbb{Z}}_3$-NMSSM featuring a light bino-dominated LSP and a light singlet-like scalar under the LZ Experiment
• Benchmark Planes for Higgs-to-Higgs Decays in the NMSSM
• Shedding Light on Dark Matter at the LHC with Machine Learning