Journal Article

·2026 OPEN ACCESS

Search for low-mass hidden-valley dark showers with non-prompt muon pairs in proton-proton collisions at $$ \sqrt{s}=13 $$ TeV

A. Hayrapetyan , Vladimir Makarenko , A. Tumasyan , W. Adam , J. W. Andrejkovic , L. Benato , T. Bergauer , M. Dragicevic , C. Giordano , P. S. Hussain ,

Journal of High Energy Physics

Abstract

A bstract A search for signatures of a dark analog to quantum chromodynamics is performed. The analysis targets long-lived dark mesons that decay into standard-model particles, with a high branching fraction of the dark mesons decaying into muons. The dark mesons are formed by the hadronisation of dark partons, which are produced by a decay of the Higgs boson. The search is performed using a data set corresponding to an integrated luminosity of 41.6 fb − 1 , which was collected in proton-proton collisions at $$ \sqrt{s}=13 $$ s = 13 TeV by the CMS experiment at the CERN LHC in 2018 using non-prompt muon triggers. The search is based on resonant muon pair signatures. Machine-learning techniques are employed in the analysis, utilising boosted decision trees to discriminate between signal and background. No significant excess is observed above the standard model expectation. Upper limits on the branching fraction of the Higgs boson decaying to dark partons are determined to be as low as 10 − 4 at 95% confidence level, surpassing and extending the existing limits on models with dark $$ \overset{\sim }{\upomega} $$ ω ~ mesons for mean proper decay lengths of less than 500 mm and for $$ \overset{\sim }{\upomega} $$ ω ~ masses down to 0.3 GeV. First limits are set for extended dark-shower models with two dark flavours that contain dark photons, probing their masses down to 0.33 GeV.

Keywords

Muon Large Hadron Collider Higgs boson Meson Branching fraction Dark matter Standard Model (mathematical formulation) Physics beyond the Standard Model Physics Particle physics Nuclear physics

Subject Areas

Particle physics theoretical and experimental studies ·Nuclear and High Energy Physics ·Physical Sciences
High-Energy Particle Collisions Research ·Nuclear and High Energy Physics ·Physical Sciences
Computational Physics and Python Applications ·Artificial Intelligence ·Physical Sciences

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