Preprint

·2026 OPEN ACCESS

Combination and interpretation of differential Higgs boson production cross sections in proton-proton collisions at $$ \sqrt{s}=13 $$ TeV

Vladimir Chekhovsky , A. Hayrapetyan , В. Макаренко , A. Tumasyan , Wolfgang Adam , Janik Walter Andrejkovic , L. Benato , Thomas Bergauer , S. Chatterjee , K. Damanakis ,

Journal of High Energy Physics

Abstract

A bstract Precision measurements of Higgs boson differential production cross sections are a key tool to probe the properties of the Higgs boson and test the standard model. New physics can affect both Higgs boson production and decay, leading to deviations from the distributions that are expected in the standard model. In this paper, combined measurements of differential spectra in a fiducial region matching the experimental selections are performed, based on analyses of four Higgs boson decay channels (γγ, ZZ (*) , WW (*) , and ττ) using proton-proton collision data recorded with the CMS detector at $$ \sqrt{s}=13 $$ s = 13 TeV, corresponding to an integrated luminosity of 138 fb − 1 . The differential measurements are extrapolated to the full phase space and combined to provide the differential spectra. A measurement of the total Higgs boson production cross section is also performed using the γγ and ZZ decay channels, with a result of $$ {53.4}_{-2.9}^{+2.9}{\left(\textrm{stat}\right)}_{-1.8}^{+1.9}\left(\textrm{syst}\right) $$ 53.4 − 2.9 + 2.9 stat − 1.8 + 1.9 syst pb, consistent with the standard model prediction of 55.6 ± 2.5 pb. The fiducial measurements are used to compute limits on Higgs boson couplings using the κ -framework and the SM effective field theory.

Keywords

Higgs boson Standard Model (mathematical formulation) Luminosity Boson Vector boson Large Hadron Collider Production (economics) 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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