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Free, publicly-accessible full text available May 1, 2023
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A bstract A search for a heavy resonance decaying into a top quark and a W boson in proton-proton collisions at $$ \sqrt{s} $$ s = 13 TeV is presented. The data analyzed were recorded with the CMS detector at the LHC and correspond to an integrated luminosity of 138 fb − 1 . The top quark is reconstructed as a single jet and the W boson, from its decay into an electron or muon and the corresponding neutrino. A top quark tagging technique based on jet clustering with a variable distance parameter and simultaneous jet grooming is used tomore »Free, publicly-accessible full text available April 1, 2023
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A bstract A search for a heavy resonance decaying to a top quark and a W boson in the fully hadronic final state is presented. The analysis is performed using data from proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 137 fb − 1 recorded by the CMS experiment at the LHC. The search is focused on heavy resonances, where the decay products of each top quark or W boson are expected to be reconstructed as a single, large-radius jet with a distinct substructure. The production of an excited bottom quark, b *more »Free, publicly-accessible full text available December 1, 2022
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Free, publicly-accessible full text available September 1, 2022
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null (Ed.)Abstract Production cross sections of the Higgs boson are measured in the $${\mathrm{H}} \rightarrow {\mathrm{Z}} {\mathrm{Z}} \rightarrow 4\ell $$ H → Z Z → 4 ℓ ( $$\ell ={\mathrm{e}},{{{\upmu }}_{\mathrm{}}^{\mathrm{}}} $$ ℓ = e , μ ) decay channel. A data sample of proton–proton collisions at a center-of-mass energy of 13 $$\,\text {Te}\text {V}$$ Te , collected by the CMS detector at the LHC and corresponding to an integrated luminosity of 137 $$\,\text {fb}^{-1}$$ fb - 1 is used. The signal strength modifier $$\mu $$ μ , defined as the ratio of the Higgs boson production rate in the $$4\ellmore »
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A bstract There has been substantial progress in applying machine learning techniques to classification problems in collider and jet physics. But as these techniques grow in sophistication, they are becoming more sensitive to subtle features of jets that may not be well modeled in simulation. Therefore, relying on simulations for training will lead to sub-optimal performance in data, but the lack of true class labels makes it difficult to train on real data. To address this challenge we introduce a new approach, called Tag N’ Train (TNT), that can be applied to unlabeled data that has two distinct sub-objects. Themore »