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| CMS-PAS-HIG-24-008 | ||
| Search for Higgs boson production at high transverse momentum in the $ \mathrm{WW^{*}} $ decay channel in proton-proton collisions at $ \sqrt{s} = $ 13 TeV | ||
| CMS Collaboration | ||
| 21 May 2025 | ||
| Abstract: A search for Higgs boson production at high transverse momentum in the $ \mathrm{WW^{*}} $ decay channel is presented. The analysis uses proton-proton collision data collected with the CMS detector at $ \sqrt{s} = $ 13 TeV during the years 2016--2018, corresponding to an integrated luminosity of 138 fb$ ^{-1} $. Both semileptonic and fully hadronic decay modes are considered, focusing on final states with either one isolated lepton (1 $ \ell $) or none (0 $ \ell $), where the Higgs boson decay products are reconstructed within a single large-radius jet. Higgs boson candidate jets are identified using the particle transformer algorithm, which is calibrated via the Lund jet plane technique and fine-tuned to improve the expected signal significance in the 1 $ \ell $ channel. The 0 $ \ell $ channel considers all Higgs boson production modes inclusively, while the 1 $ \ell $ channel further categorizes events into the two dominant production mechanisms: gluon fusion and vector boson fusion. The signal strength, defined as the ratio of the observed signal to the standard model expectation, is $ \mu_{\mathrm{H}} = $ 0.01 $ ^{+0.63}_{-0.48} $, with no signal observed above the background. The result represents the first measurement in this decay mode and is consistent with those obtained in other final states. | ||
| Links: CDS record (PDF) ; CADI line (restricted) ; | ||
| Figures | |
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Figure 1:
Illustration of the event topologies analyzed in this study. On the left, the dominant H boson production modes, from top to bottom: gluon fusion (ggF) and vector boson fusion (VBF). Additional jets from initial or final state radiation are shown in yellow cones for 0 $ \ell $ and 1 $ \ell $ ggF, while for 1 $ \ell $ VBF, a pair of extra jets is shown in purple. On the right, the various final states resulting from $ \mathrm{H}\to\mathrm{W}\mathrm{W}^{*}\to\ell\nu\mathrm{q}\mathrm{q}/\mathrm{q} \mathrm{q}\mathrm{q}\mathrm{q} $ decays. |
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Figure 2:
Performance curves showing the misidentification probability of background jets versus the identification probability of $ \mathrm{H} \to \mathrm{W} \mathrm{W}^{*} $ resonance jets for PART and PART-FINETUNED. Left: Discrimination performance of the PART model for various $ \mathrm{H} \to \mathrm{W} \mathrm{W}^{*} $ decays against the dominant QCD multijet background, following a selection similar to that of the 0 $ \ell $ channel. Right: Comparison of $ P(\mathrm{H}_{1\ell}) $ before and after fine-tuning, following the event selection in the 1 $ \ell $ channel. After fine-tuning, $ P(\mathrm{H}_{1\ell}) $ provides significantly better discrimination in the 1 $ \ell $ final state, achieving 60% higher signal efficiency at a background misidentification rate of $ 10^{-2} $. The background includes jets originating from QCD multijet events, $ \mathrm{W}(\ell\nu) $+jets, and top quark processes. |
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Figure 2-a:
Performance curves showing the misidentification probability of background jets versus the identification probability of $ \mathrm{H} \to \mathrm{W} \mathrm{W}^{*} $ resonance jets for PART and PART-FINETUNED. Left: Discrimination performance of the PART model for various $ \mathrm{H} \to \mathrm{W} \mathrm{W}^{*} $ decays against the dominant QCD multijet background, following a selection similar to that of the 0 $ \ell $ channel. Right: Comparison of $ P(\mathrm{H}_{1\ell}) $ before and after fine-tuning, following the event selection in the 1 $ \ell $ channel. After fine-tuning, $ P(\mathrm{H}_{1\ell}) $ provides significantly better discrimination in the 1 $ \ell $ final state, achieving 60% higher signal efficiency at a background misidentification rate of $ 10^{-2} $. The background includes jets originating from QCD multijet events, $ \mathrm{W}(\ell\nu) $+jets, and top quark processes. |
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Figure 2-b:
Performance curves showing the misidentification probability of background jets versus the identification probability of $ \mathrm{H} \to \mathrm{W} \mathrm{W}^{*} $ resonance jets for PART and PART-FINETUNED. Left: Discrimination performance of the PART model for various $ \mathrm{H} \to \mathrm{W} \mathrm{W}^{*} $ decays against the dominant QCD multijet background, following a selection similar to that of the 0 $ \ell $ channel. Right: Comparison of $ P(\mathrm{H}_{1\ell}) $ before and after fine-tuning, following the event selection in the 1 $ \ell $ channel. After fine-tuning, $ P(\mathrm{H}_{1\ell}) $ provides significantly better discrimination in the 1 $ \ell $ final state, achieving 60% higher signal efficiency at a background misidentification rate of $ 10^{-2} $. The background includes jets originating from QCD multijet events, $ \mathrm{W}(\ell\nu) $+jets, and top quark processes. |
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Figure 3:
The event distributions for the total simulated background and total signal (scaled by a factor of 3 $ \times10^4 $) passing the event selection for the 0 $ \ell $ channel. The signal is decomposed into classes as defined in the text. The upper-left and upper-right panels show the soft-drop mass and PART score distributions for the H candidate jet (j) $ P(\mathrm{H}_{0\ell}) $, respectively. The lower-left and lower-right panels display the $ p_{\mathrm{T}}^\text{miss}/p_{\mathrm{T}}^{\text{j}} $ ratio and the angle $ |\Delta\phi (j, {\vec p}_{\mathrm{T}}^{\kern1pt\text{miss}}) | $, respectively. Vertical lines indicate the selection conditions imposed to define the signal region. |
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Figure 3-a:
The event distributions for the total simulated background and total signal (scaled by a factor of 3 $ \times10^4 $) passing the event selection for the 0 $ \ell $ channel. The signal is decomposed into classes as defined in the text. The upper-left and upper-right panels show the soft-drop mass and PART score distributions for the H candidate jet (j) $ P(\mathrm{H}_{0\ell}) $, respectively. The lower-left and lower-right panels display the $ p_{\mathrm{T}}^\text{miss}/p_{\mathrm{T}}^{\text{j}} $ ratio and the angle $ |\Delta\phi (j, {\vec p}_{\mathrm{T}}^{\kern1pt\text{miss}}) | $, respectively. Vertical lines indicate the selection conditions imposed to define the signal region. |
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Figure 3-b:
The event distributions for the total simulated background and total signal (scaled by a factor of 3 $ \times10^4 $) passing the event selection for the 0 $ \ell $ channel. The signal is decomposed into classes as defined in the text. The upper-left and upper-right panels show the soft-drop mass and PART score distributions for the H candidate jet (j) $ P(\mathrm{H}_{0\ell}) $, respectively. The lower-left and lower-right panels display the $ p_{\mathrm{T}}^\text{miss}/p_{\mathrm{T}}^{\text{j}} $ ratio and the angle $ |\Delta\phi (j, {\vec p}_{\mathrm{T}}^{\kern1pt\text{miss}}) | $, respectively. Vertical lines indicate the selection conditions imposed to define the signal region. |
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Figure 3-c:
The event distributions for the total simulated background and total signal (scaled by a factor of 3 $ \times10^4 $) passing the event selection for the 0 $ \ell $ channel. The signal is decomposed into classes as defined in the text. The upper-left and upper-right panels show the soft-drop mass and PART score distributions for the H candidate jet (j) $ P(\mathrm{H}_{0\ell}) $, respectively. The lower-left and lower-right panels display the $ p_{\mathrm{T}}^\text{miss}/p_{\mathrm{T}}^{\text{j}} $ ratio and the angle $ |\Delta\phi (j, {\vec p}_{\mathrm{T}}^{\kern1pt\text{miss}}) | $, respectively. Vertical lines indicate the selection conditions imposed to define the signal region. |
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Figure 3-d:
The event distributions for the total simulated background and total signal (scaled by a factor of 3 $ \times10^4 $) passing the event selection for the 0 $ \ell $ channel. The signal is decomposed into classes as defined in the text. The upper-left and upper-right panels show the soft-drop mass and PART score distributions for the H candidate jet (j) $ P(\mathrm{H}_{0\ell}) $, respectively. The lower-left and lower-right panels display the $ p_{\mathrm{T}}^\text{miss}/p_{\mathrm{T}}^{\text{j}} $ ratio and the angle $ |\Delta\phi (j, {\vec p}_{\mathrm{T}}^{\kern1pt\text{miss}}) | $, respectively. Vertical lines indicate the selection conditions imposed to define the signal region. |
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Figure 4:
Illustration of the SRs and CRs, and the TFs used to relate the QCD background in the different regions (left). The TFs used to predict the QCD process in the four SRs as a function of the $ m^*_{\text{j}} $ (right). |
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Figure 4-a:
Illustration of the SRs and CRs, and the TFs used to relate the QCD background in the different regions (left). The TFs used to predict the QCD process in the four SRs as a function of the $ m^*_{\text{j}} $ (right). |
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Figure 4-b:
Illustration of the SRs and CRs, and the TFs used to relate the QCD background in the different regions (left). The TFs used to predict the QCD process in the four SRs as a function of the $ m^*_{\text{j}} $ (right). |
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Figure 5:
Post-fit $ m^*_{\text{j}} $ distributions for the predicted background, observed data, and signal in the SRs of the 0 $ \ell $ channel. From upper to lower and left to right, the plots correspond to $ \text{SR}_\text{1a} $, $ \text{SR}_\text{2a} $, $ \text{SR}_\text{1b} $, and $ \text{SR}_\text{2b} $, respectively. The lower panel of each plot presents the pull distribution, as well as the $ \sigma_\text{syst} $ normalized to the $ \sigma_\text{stat} $. The signal is scaled by its best-fit value, $ \mu_\mathrm{H} = $ 0.01, from the combined 0 $ \ell $ and 1 $ \ell $ channels, and by an additional factor (as labeled) for visibility. |
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Figure 5-a:
Post-fit $ m^*_{\text{j}} $ distributions for the predicted background, observed data, and signal in the SRs of the 0 $ \ell $ channel. From upper to lower and left to right, the plots correspond to $ \text{SR}_\text{1a} $, $ \text{SR}_\text{2a} $, $ \text{SR}_\text{1b} $, and $ \text{SR}_\text{2b} $, respectively. The lower panel of each plot presents the pull distribution, as well as the $ \sigma_\text{syst} $ normalized to the $ \sigma_\text{stat} $. The signal is scaled by its best-fit value, $ \mu_\mathrm{H} = $ 0.01, from the combined 0 $ \ell $ and 1 $ \ell $ channels, and by an additional factor (as labeled) for visibility. |
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Figure 5-b:
Post-fit $ m^*_{\text{j}} $ distributions for the predicted background, observed data, and signal in the SRs of the 0 $ \ell $ channel. From upper to lower and left to right, the plots correspond to $ \text{SR}_\text{1a} $, $ \text{SR}_\text{2a} $, $ \text{SR}_\text{1b} $, and $ \text{SR}_\text{2b} $, respectively. The lower panel of each plot presents the pull distribution, as well as the $ \sigma_\text{syst} $ normalized to the $ \sigma_\text{stat} $. The signal is scaled by its best-fit value, $ \mu_\mathrm{H} = $ 0.01, from the combined 0 $ \ell $ and 1 $ \ell $ channels, and by an additional factor (as labeled) for visibility. |
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Figure 5-c:
Post-fit $ m^*_{\text{j}} $ distributions for the predicted background, observed data, and signal in the SRs of the 0 $ \ell $ channel. From upper to lower and left to right, the plots correspond to $ \text{SR}_\text{1a} $, $ \text{SR}_\text{2a} $, $ \text{SR}_\text{1b} $, and $ \text{SR}_\text{2b} $, respectively. The lower panel of each plot presents the pull distribution, as well as the $ \sigma_\text{syst} $ normalized to the $ \sigma_\text{stat} $. The signal is scaled by its best-fit value, $ \mu_\mathrm{H} = $ 0.01, from the combined 0 $ \ell $ and 1 $ \ell $ channels, and by an additional factor (as labeled) for visibility. |
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Figure 5-d:
Post-fit $ m^*_{\text{j}} $ distributions for the predicted background, observed data, and signal in the SRs of the 0 $ \ell $ channel. From upper to lower and left to right, the plots correspond to $ \text{SR}_\text{1a} $, $ \text{SR}_\text{2a} $, $ \text{SR}_\text{1b} $, and $ \text{SR}_\text{2b} $, respectively. The lower panel of each plot presents the pull distribution, as well as the $ \sigma_\text{syst} $ normalized to the $ \sigma_\text{stat} $. The signal is scaled by its best-fit value, $ \mu_\mathrm{H} = $ 0.01, from the combined 0 $ \ell $ and 1 $ \ell $ channels, and by an additional factor (as labeled) for visibility. |
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Figure 6:
Post-fit $ m^*_{\text{j}} $ distributions for the predicted background, observed data, and signal, decomposed by production mode, in the SRs of the 1 $ \ell $ channel. Upper to lower row, and left to right: Top CR, W+jets CR, VBF SR, and the ggF SRs binned in $ p_{\mathrm{T}} $ as $ [250,350) $, $ [350,500) $, and $ [500,+\infty) $ GeV, respectively. The lower panel of each plot presents the pull distribution, as well as $ \sigma_\text{syst} $ normalized to the $ \sigma_\text{stat} $. The signal is scaled by its best-fit value, $ \mu_\mathrm{H} = $ 0.01, from the combined 0 $ \ell $ and 1 $ \ell $ channels, and by an additional factor (as labeled) for visibility |
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Figure 6-a:
Post-fit $ m^*_{\text{j}} $ distributions for the predicted background, observed data, and signal, decomposed by production mode, in the SRs of the 1 $ \ell $ channel. Upper to lower row, and left to right: Top CR, W+jets CR, VBF SR, and the ggF SRs binned in $ p_{\mathrm{T}} $ as $ [250,350) $, $ [350,500) $, and $ [500,+\infty) $ GeV, respectively. The lower panel of each plot presents the pull distribution, as well as $ \sigma_\text{syst} $ normalized to the $ \sigma_\text{stat} $. The signal is scaled by its best-fit value, $ \mu_\mathrm{H} = $ 0.01, from the combined 0 $ \ell $ and 1 $ \ell $ channels, and by an additional factor (as labeled) for visibility |
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Figure 6-b:
Post-fit $ m^*_{\text{j}} $ distributions for the predicted background, observed data, and signal, decomposed by production mode, in the SRs of the 1 $ \ell $ channel. Upper to lower row, and left to right: Top CR, W+jets CR, VBF SR, and the ggF SRs binned in $ p_{\mathrm{T}} $ as $ [250,350) $, $ [350,500) $, and $ [500,+\infty) $ GeV, respectively. The lower panel of each plot presents the pull distribution, as well as $ \sigma_\text{syst} $ normalized to the $ \sigma_\text{stat} $. The signal is scaled by its best-fit value, $ \mu_\mathrm{H} = $ 0.01, from the combined 0 $ \ell $ and 1 $ \ell $ channels, and by an additional factor (as labeled) for visibility |
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Figure 6-c:
Post-fit $ m^*_{\text{j}} $ distributions for the predicted background, observed data, and signal, decomposed by production mode, in the SRs of the 1 $ \ell $ channel. Upper to lower row, and left to right: Top CR, W+jets CR, VBF SR, and the ggF SRs binned in $ p_{\mathrm{T}} $ as $ [250,350) $, $ [350,500) $, and $ [500,+\infty) $ GeV, respectively. The lower panel of each plot presents the pull distribution, as well as $ \sigma_\text{syst} $ normalized to the $ \sigma_\text{stat} $. The signal is scaled by its best-fit value, $ \mu_\mathrm{H} = $ 0.01, from the combined 0 $ \ell $ and 1 $ \ell $ channels, and by an additional factor (as labeled) for visibility |
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Figure 6-d:
Post-fit $ m^*_{\text{j}} $ distributions for the predicted background, observed data, and signal, decomposed by production mode, in the SRs of the 1 $ \ell $ channel. Upper to lower row, and left to right: Top CR, W+jets CR, VBF SR, and the ggF SRs binned in $ p_{\mathrm{T}} $ as $ [250,350) $, $ [350,500) $, and $ [500,+\infty) $ GeV, respectively. The lower panel of each plot presents the pull distribution, as well as $ \sigma_\text{syst} $ normalized to the $ \sigma_\text{stat} $. The signal is scaled by its best-fit value, $ \mu_\mathrm{H} = $ 0.01, from the combined 0 $ \ell $ and 1 $ \ell $ channels, and by an additional factor (as labeled) for visibility |
png pdf |
Figure 6-e:
Post-fit $ m^*_{\text{j}} $ distributions for the predicted background, observed data, and signal, decomposed by production mode, in the SRs of the 1 $ \ell $ channel. Upper to lower row, and left to right: Top CR, W+jets CR, VBF SR, and the ggF SRs binned in $ p_{\mathrm{T}} $ as $ [250,350) $, $ [350,500) $, and $ [500,+\infty) $ GeV, respectively. The lower panel of each plot presents the pull distribution, as well as $ \sigma_\text{syst} $ normalized to the $ \sigma_\text{stat} $. The signal is scaled by its best-fit value, $ \mu_\mathrm{H} = $ 0.01, from the combined 0 $ \ell $ and 1 $ \ell $ channels, and by an additional factor (as labeled) for visibility |
png pdf |
Figure 6-f:
Post-fit $ m^*_{\text{j}} $ distributions for the predicted background, observed data, and signal, decomposed by production mode, in the SRs of the 1 $ \ell $ channel. Upper to lower row, and left to right: Top CR, W+jets CR, VBF SR, and the ggF SRs binned in $ p_{\mathrm{T}} $ as $ [250,350) $, $ [350,500) $, and $ [500,+\infty) $ GeV, respectively. The lower panel of each plot presents the pull distribution, as well as $ \sigma_\text{syst} $ normalized to the $ \sigma_\text{stat} $. The signal is scaled by its best-fit value, $ \mu_\mathrm{H} = $ 0.01, from the combined 0 $ \ell $ and 1 $ \ell $ channels, and by an additional factor (as labeled) for visibility |
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Figure 7:
Observed scan of the profile likelihood test statistic $ t_\mu $ as a function of the signal strength $ \mu_\mathrm{H} $ for the combined 0 $ \ell $ and 1 $ \ell $ channels. The solid lines correspond to profiling all statistical and systematic uncertainties, while the dashed lines correspond to profiling only the statistical uncertainties. |
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Figure 8:
Fitted signal strength (left) and significance (right) for $ \mathrm{H} \to \mathrm{W} \mathrm{W}^{*} $ in the 0 $ \ell $ and 1 $ \ell $ channels using the full dataset. Combined results are presented alongside individual contributions from each channel. For the 1 $ \ell $ channel, results are also shown for an alternative measurement using five independent signal strengths for the ggF and VBF processes, corresponding to different generator-level selections. Expected uncertainties are indicated by yellow (signal strength) and light blue (significance) bands. Blue and black lines represent statistical and total observed uncertainties, respectively. The results are consistent with standard model expectations. |
| Tables | |
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Table 1:
The 37 PART jet classification categories. The categories are based on the decay modes of H and V bosons, top quarks, and QCD processes. Numbers in parentheses (e.g., 0 c, 1 c) indicate the number of c quarks present in a given final state in front of it. All listed decay products are assumed to be contained within the jet cone, except for neutrinos. For example, labels such as 3 $ \mathrm{q} $ or $ \mathrm{b}\mathrm{q} $ imply that one quark escapes the jet cone in $ \mathrm{H}\to4\mathrm{q} $ or $ \mathrm{t}\to\mathrm{b}\mathrm{q}\mathrm{q} $ decays, respectively. |
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Table 2:
Kinematic requirements used to define the signal regions (SRs) and control regions (CRs) in the 0 $ \ell $ channel. The right columns list the conditions under which the $ m_{\text{j}} $ is replaced by the corrected $ m^*_{\text{j}} $ mass. The ``&" represents the logical ``AND" and $ \Delta\phi $ refers to the azimuthal angle difference between $ {\vec p}_{\mathrm{T}}^{\kern1pt\text{miss}} $ and the transverse momentum vector of the H boson candidate. |
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Table 3:
Systematic uncertainty sources considered in the analysis. Left to right columns: the sources, the channels applied, whether the uncertainty affects signal (S) or background (B), its influence on shape (s) or rate (r), whether the nuisances are (un)correlated (u or $ \checkmark $) among different process models (P) or among the data-taking years (Y). |
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Table 4:
Observed and expected fitted signal strength (second column) and significance (third column) for $ \mathrm{H} \to \mathrm{W} \mathrm{W}^{*} $ in the 0 $ \ell $ and 1 $ \ell $ channels, followed by the combined result. |
| Summary |
| A search for Higgs boson (H) production at high transverse momentum in the $ \mathrm{H} \to \mathrm{W} \mathrm{W}^{*} $ decay channel is presented. The analysis uses proton-proton collision data collected at $ \sqrt{s} = $ 13 TeV with the CMS experiment, corresponding to an integrated luminosity of 138 fb$^{-1}$, and focuses on semileptonic decays with or without an isolated lepton in the final state. The final states are characterized by a single large-radius jet containing the hadronic decay products of the W bosons, exploiting the jet substructure resulting from the boosted topology of the Higgs boson decay. The 1 $ \ell $ channel categorizes events by the dominant Higgs boson production mechanisms: gluon-gluon fusion and vector boson fusion, while the 0 $ \ell $ channel remains inclusive across all production modes. The particle transformer algorithm leverages advanced machine learning techniques to identify H candidate jets with intricate substructure, missing transverse momentum aligned with the jet, or leptons inside the jet. It is calibrated with the Lund jet plane method, and fine-tuned to optimize the expected signal significance in the 1 $ \ell $ channel. The invariant mass of the Higgs or vector boson candidate jet is used for signal extraction. The expected signal significance is 1.76$ \sigma $, while the observed signal strength relative to the standard model expectation is $ \mu_{\mathrm{H}} = $ 0.01 $ ^{+0.63}_{-0.48} $, showing no evidence of a signal above the background. These measurements represent the first dedicated study of highly Lorentz-boosted $ \mathrm{H} \to \mathrm{W} \mathrm{W}^{*} $ decays, complementing earlier searches in other Higgs boson decay channels and production modes. |
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