Journal Article

·2026 OPEN ACCESS

High-precision measurement of the W boson mass with the CMS experiment

V. Chekhovsky , A. Hayrapetyan , В. Макаренко , A. Tumasyan , W. Adam , J. W. Andrejkovic , L. Benato , T. Bergauer , S. Chatterjee , K. Damanakis ,

Nature

Abstract

In the standard model of particle physics, the masses of the W and Z bosons, the carriers of the weak interaction, are uniquely related. A precise determination of their masses is important because quantum loops of heavy, undiscovered particles could modify this relationship. Although the Z mass is known to the remarkable precision of 22 parts per million (2.0 MeV), the W mass is known much less precisely. A global fit to measured electroweak observables predicts the W mass with 6 MeV uncertainty1-3. Reaching a comparable experimental precision would be a sensitive and fundamental test of the standard model, made even more urgent by a recent challenge to the global fit prediction by a measurement from the CDF Collaboration at the Fermilab Tevatron collider4. Here we report the measurement of the W mass by the CMS Collaboration at the CERN Large Hadron Collider, based on a large data sample of W → μν events collected in 2016 at the proton-proton collision energy of 13 TeV. The measurement exploits a high-granularity maximum likelihood fit to the kinematic properties of muons produced in W decays. By combining an accurate determination of experimental effects with marked in situ constraints of theoretical inputs, we reach a precise measurement of the W mass, of 80,360.2 ± 9.9 MeV, in agreement with the standard model prediction.

Keywords

Tevatron Electroweak interaction Large Hadron Collider Standard Model (mathematical formulation) Fermilab Muon Collider 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
Quantum Chromodynamics and Particle Interactions ·Nuclear and High Energy Physics ·Physical Sciences
Computational Physics and Python Applications ·Artificial Intelligence ·Physical Sciences

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