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| CMS-PAS-HIG-24-011 | ||
| Measurement of the Higgs boson decay width from off-shell production using the WW $ \to $ 2$\ell$ 2$\nu $ final state in proton-proton collisions at 13 TeV | ||
| CMS Collaboration | ||
| 2025-10-22 | ||
| Abstract: We constrain the Higgs decay width using the off-shell production of Higgs bosons in their decay H $ \to $ WW $ \to $2$\ell $2$\nu $. The analysis uses proton-proton collision data with an integrated luminosity of 138 fb$ ^{-1} $ collected at a center-of-mass energy of $ \sqrt{s}= $ 13 TeV by the CMS experiment at the LHC. On- and off-shell signal strengths are used to derive the Higgs boson total decay width as $ \Gamma_{\mathrm{H}} = $ 3.9 $ ^{+2.7}_{-2.2} $ MeV, in agreement with the standard model value. This is an order of magnitude improvement over previous CMS results in this decay channel. | ||
| Links: CDS record (PDF) ; CADI line (restricted) ; | ||
| Figures | |
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Figure 1:
Example of feynman diagrams for Higgs boson production and decays to WW in ggF (left) and VBF (right). |
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Figure 1-a:
Example of feynman diagrams for Higgs boson production and decays to WW in ggF (left) and VBF (right). |
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Figure 1-b:
Example of feynman diagrams for Higgs boson production and decays to WW in ggF (left) and VBF (right). |
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Figure 2:
Feynman diagrams for continuum WW production: $ \mathrm{g}\mathrm{g} \to \mathrm{W}\mathrm{W} $ (left) and $ \mathrm{q}\mathrm{q} \to \mathrm{q}\mathrm{q}\mathrm{W}\mathrm{W} $ (right). |
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Figure 2-a:
Feynman diagrams for continuum WW production: $ \mathrm{g}\mathrm{g} \to \mathrm{W}\mathrm{W} $ (left) and $ \mathrm{q}\mathrm{q} \to \mathrm{q}\mathrm{q}\mathrm{W}\mathrm{W} $ (right). |
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Figure 2-b:
Feynman diagrams for continuum WW production: $ \mathrm{g}\mathrm{g} \to \mathrm{W}\mathrm{W} $ (left) and $ \mathrm{q}\mathrm{q} \to \mathrm{q}\mathrm{q}\mathrm{W}\mathrm{W} $ (right). |
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Figure 3:
Post-fit DNN output distributions in each off-shell SR. From upper left to lower right: (upper left) 2-jet DNN score for VBF SR, (upper right) 2-jet DNN score for ggF SR, (lower left) 1-jet DNN score for ggF SR, and (lower right) 0-jet DNN score for ggF SR. The ggF H off-shell S+B+I and VBF H off-shell S+B+I contributions are displayed both as part of the total stacked prediction with all other processes in the legend, and again as separate lines that are stacked with respect to each other but unstacked from the other processes, overlaid on top of the stacked background for visibility. The Data/Pred ratio includes the post-fit signal. |
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Figure 3-a:
Post-fit DNN output distributions in each off-shell SR. From upper left to lower right: (upper left) 2-jet DNN score for VBF SR, (upper right) 2-jet DNN score for ggF SR, (lower left) 1-jet DNN score for ggF SR, and (lower right) 0-jet DNN score for ggF SR. The ggF H off-shell S+B+I and VBF H off-shell S+B+I contributions are displayed both as part of the total stacked prediction with all other processes in the legend, and again as separate lines that are stacked with respect to each other but unstacked from the other processes, overlaid on top of the stacked background for visibility. The Data/Pred ratio includes the post-fit signal. |
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Figure 3-b:
Post-fit DNN output distributions in each off-shell SR. From upper left to lower right: (upper left) 2-jet DNN score for VBF SR, (upper right) 2-jet DNN score for ggF SR, (lower left) 1-jet DNN score for ggF SR, and (lower right) 0-jet DNN score for ggF SR. The ggF H off-shell S+B+I and VBF H off-shell S+B+I contributions are displayed both as part of the total stacked prediction with all other processes in the legend, and again as separate lines that are stacked with respect to each other but unstacked from the other processes, overlaid on top of the stacked background for visibility. The Data/Pred ratio includes the post-fit signal. |
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Figure 3-c:
Post-fit DNN output distributions in each off-shell SR. From upper left to lower right: (upper left) 2-jet DNN score for VBF SR, (upper right) 2-jet DNN score for ggF SR, (lower left) 1-jet DNN score for ggF SR, and (lower right) 0-jet DNN score for ggF SR. The ggF H off-shell S+B+I and VBF H off-shell S+B+I contributions are displayed both as part of the total stacked prediction with all other processes in the legend, and again as separate lines that are stacked with respect to each other but unstacked from the other processes, overlaid on top of the stacked background for visibility. The Data/Pred ratio includes the post-fit signal. |
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Figure 3-d:
Post-fit DNN output distributions in each off-shell SR. From upper left to lower right: (upper left) 2-jet DNN score for VBF SR, (upper right) 2-jet DNN score for ggF SR, (lower left) 1-jet DNN score for ggF SR, and (lower right) 0-jet DNN score for ggF SR. The ggF H off-shell S+B+I and VBF H off-shell S+B+I contributions are displayed both as part of the total stacked prediction with all other processes in the legend, and again as separate lines that are stacked with respect to each other but unstacked from the other processes, overlaid on top of the stacked background for visibility. The Data/Pred ratio includes the post-fit signal. |
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Figure 4:
Post-fit DNN output distributions in each on-shell CR. From upper left to lower right: (upper left) 2-jet DNN score for VBF CR, (upper right) 2-jet DNN score for ggF CR, (lower left) 1-jet DNN score for ggF CR, and (lower right) 0-jet DNN score for ggF CR. The ggF H off-shell S+B+I and VBF H off-shell S+B+I contributions are displayed both as part of the total stacked prediction with all other processes in the legend, and again as separate lines that are stacked with respect to each other but unstacked from the other processes, overlaid on top of the stacked background for visibility. The Data/Pred ratio includes the post-fit signal. Note that in the ggF on-shell distributions, VBF off-shell events will most likely not populate these regions, which causes the lack of VBF off-shell S+B+I. |
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Figure 4-a:
Post-fit DNN output distributions in each on-shell CR. From upper left to lower right: (upper left) 2-jet DNN score for VBF CR, (upper right) 2-jet DNN score for ggF CR, (lower left) 1-jet DNN score for ggF CR, and (lower right) 0-jet DNN score for ggF CR. The ggF H off-shell S+B+I and VBF H off-shell S+B+I contributions are displayed both as part of the total stacked prediction with all other processes in the legend, and again as separate lines that are stacked with respect to each other but unstacked from the other processes, overlaid on top of the stacked background for visibility. The Data/Pred ratio includes the post-fit signal. Note that in the ggF on-shell distributions, VBF off-shell events will most likely not populate these regions, which causes the lack of VBF off-shell S+B+I. |
png pdf |
Figure 4-b:
Post-fit DNN output distributions in each on-shell CR. From upper left to lower right: (upper left) 2-jet DNN score for VBF CR, (upper right) 2-jet DNN score for ggF CR, (lower left) 1-jet DNN score for ggF CR, and (lower right) 0-jet DNN score for ggF CR. The ggF H off-shell S+B+I and VBF H off-shell S+B+I contributions are displayed both as part of the total stacked prediction with all other processes in the legend, and again as separate lines that are stacked with respect to each other but unstacked from the other processes, overlaid on top of the stacked background for visibility. The Data/Pred ratio includes the post-fit signal. Note that in the ggF on-shell distributions, VBF off-shell events will most likely not populate these regions, which causes the lack of VBF off-shell S+B+I. |
png pdf |
Figure 4-c:
Post-fit DNN output distributions in each on-shell CR. From upper left to lower right: (upper left) 2-jet DNN score for VBF CR, (upper right) 2-jet DNN score for ggF CR, (lower left) 1-jet DNN score for ggF CR, and (lower right) 0-jet DNN score for ggF CR. The ggF H off-shell S+B+I and VBF H off-shell S+B+I contributions are displayed both as part of the total stacked prediction with all other processes in the legend, and again as separate lines that are stacked with respect to each other but unstacked from the other processes, overlaid on top of the stacked background for visibility. The Data/Pred ratio includes the post-fit signal. Note that in the ggF on-shell distributions, VBF off-shell events will most likely not populate these regions, which causes the lack of VBF off-shell S+B+I. |
png pdf |
Figure 4-d:
Post-fit DNN output distributions in each on-shell CR. From upper left to lower right: (upper left) 2-jet DNN score for VBF CR, (upper right) 2-jet DNN score for ggF CR, (lower left) 1-jet DNN score for ggF CR, and (lower right) 0-jet DNN score for ggF CR. The ggF H off-shell S+B+I and VBF H off-shell S+B+I contributions are displayed both as part of the total stacked prediction with all other processes in the legend, and again as separate lines that are stacked with respect to each other but unstacked from the other processes, overlaid on top of the stacked background for visibility. The Data/Pred ratio includes the post-fit signal. Note that in the ggF on-shell distributions, VBF off-shell events will most likely not populate these regions, which causes the lack of VBF off-shell S+B+I. |
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Figure 5:
The likelihood scan corresponding for the off-shell signal strength (left) and $ \Gamma_\text{H} $ (right). The 68% and 95% confidence intervals are delineated as horizontal lines. |
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Figure 5-a:
The likelihood scan corresponding for the off-shell signal strength (left) and $ \Gamma_\text{H} $ (right). The 68% and 95% confidence intervals are delineated as horizontal lines. |
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Figure 5-b:
The likelihood scan corresponding for the off-shell signal strength (left) and $ \Gamma_\text{H} $ (right). The 68% and 95% confidence intervals are delineated as horizontal lines. |
| Tables | |
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Table 1:
Measurements of off-shell signal strength $ \mu_{\text{off-shell}} $, on-shell signal strength $ \mu_{\text{on-shell}} $, and $ \Gamma_\text{H} $. Listed are the measurements of the $ \mathrm{H} \to \mathrm{W}\mathrm{W} $, $ \mu_{\text{off-shell}} $ and $ \Gamma_\text{H} $ in the 2 $ \ell 2\nu $ channel. The central value (c.v.) is reported together with the 68% and 95% confidence levels. |
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Table 2:
Uncertainties and their contribution to the parameter of interest $ r = \frac{\mu_\text{off-shell}}{\mu_\text{on-shell}} $. These contributions are the impact of each uncertainty group divided by the total uncertainty. ``Other" includes background normalization parameters and MC statistical uncertainties. The final column showcases the impact of each uncertainty group on $ \mu_\text{off-shell} $. |
| Summary |
| Measurements of the off-shell signal strengths for the Higgs boson have been performed with the VBF and ggF production modes in the $ \mathrm{H} \to \mathrm{W}\mathrm{W} $ channel, using proton-proton collision data from the CMS experiment with an integrated luminosity of 138 fb$ ^{-1} $. Specific event selection and background discrimination techniques were applied in order to extract the results from likelihood fits for all the analysis categories. The combined signal strengths from VBF and ggF for production of an off-shell Higgs boson is found to be $ \mu_\text{off-shell} = $ 1.2 $ ^{+0.8}_{-0.7} $, which is used to derive the decay width $ \Gamma_\text{H} = $ 3.9 $ ^{+2.7}_{-2.2} $ MeV at 68% confidence level, in agreement with SM expectation. This measurement represents the first CMS constraint on the Higgs boson width in the WW final state with the Run 2 dataset, improving the Run 1 limit by more than an order of magnitude and complementing the ZZ Run 2 measurements. |
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