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Search for Higgs boson production at high transversemomentum in the WW decay channel in proton-protoncollisions at √s = 13TeV
Yerevan Physics Institute, Yerevan, Armenia. (CMS Collaboration)
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. INFN Sezione di Padova, Padova, Italy. (CMS Collaboration)ORCID iD: 0000-0002-1659-8727
Institute formerly covered by a cooperation agreement with CERN. (CMS Collaboration)ORCID iD: 0000-0002-7452-8380
Number of Authors: 24962026 (English)In: Journal of High Energy Physics (JHEP), ISSN 1126-6708, E-ISSN 1029-8479, Vol. 026, no 7, article id 248Article in journal (Refereed) Published
Abstract [en]

A search for Higgs boson (H) production at high transverse momentum (pT) in the WW decay channel is presented. The analysis uses proton-proton collisions at √s = 13 TeV recorded by the CMS experiment in 2016–2018, corresponding to an integrated luminosity of 138 fb−1. The visible decay products of the Higgs boson are reconstructed as a single large-radius jet with one isolated lepton or none (1ℓ and 0ℓ, respectively; ℓ = e, μ). The H-candidate jets are identified using an advanced transformer-based algorithm and are calibrated with the Lund jet plane reweighting technique. The 1ℓ channel is further split into gluon fusion, vector boson fusion, and associated production with hadronically decaying vector boson categories, while the 0ℓ channel considers all production processes inclusively. The measured cross section times the H → WW branching fraction relative to the standard model expectation is µ = −0.19+0.48-0.46 , indicating no evidence of a signal above the background. This measurement represents the first dedicated study of highly Lorentz-boosted H → WW decays, complementing earlier searches for high-pT Higgs boson in other decay channels.

Place, publisher, year, edition, pages
Springer Nature, 2026. Vol. 026, no 7, article id 248
Keywords [en]
Hadron-Hadron Scattering, Higgs Physics, Vector Boson Production
National Category
Subatomic Physics
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-119467DOI: 10.1007/JHEP07(2026)248OAI: oai:DiVA.org:ltu-119467DiVA, id: diva2:2093338
Note

For funding information, see: https://link.springer.com/article/10.1007/JHEP07(2026)248#Ack1;

Full text license: CC BY

Available from: 2026-08-18 Created: 2026-08-18 Last updated: 2026-08-18Bibliographically approved

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Dorigo, Tommaso

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