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| CMS-HIG-24-010 ; CERN-EP-2026-101 | ||
| Improved results on Higgs boson pair production in the 4b final state | ||
| CMS Collaboration | ||
| 29 April 2026 | ||
| Submitted to Physical Review D | ||
| Abstract: Measurements of Higgs boson pair (HH) production in the four bottom quark (4b) final state are presented using proton-proton (pp) collision data at $ \sqrt{s}= $ 13.6 TeV collected by the CMS experiment at the CERN LHC, corresponding to an integrated luminosity of 62 fb$ ^{-1} $. Events in which the Higgs boson decays, $ \mathrm{H} \to \mathrm{b}\overline{\mathrm{b}} $, are separately reconstructed as pairs of small-radius jets (resolved), as well as those where they are reconstructed as single large-radius jets (merged), are studied exclusively. Benefiting from new methods in trigger selection, event selection, and signal extraction, the combination of analyses in the resolved and merged topologies gives an observed (expected) upper limit on the HH signal strength, $ \mu_{\mathrm{H}\mathrm{H}} $, of 4.4 (4.4) at 95% confidence level (CL). Compared to previously published LHC results in the 4b final state, the expected limit with an equivalent integrated luminosity is improved by more than a factor of two in the resolved topology and is better in the merged topology as well. An updated analysis of the resolved topology using 138 fb$ ^{-1} $ of 13 TeV pp collision data yields an observed (expected) 95% CL upper limit on $ \mu_{\mathrm{H}\mathrm{H}} $ of 10.0 (5.9), an improvement of about 25% in the expected limit compared to the published results using the same data. Results in the 4b final state with 13 and 13.6 TeV are combined, resulting in an observed (expected) 95% CL upper limit on $ \mu_{\mathrm{H}\mathrm{H}} $ of 4.7 (2.8). The allowed ranges for the Higgs boson trilinear self-coupling and quartic coupling between two Higgs bosons and two vector bosons are also reported. These are the most stringent constraints achieved in the 4b final state to date. | ||
| Links: e-print arXiv:2604.27044 [hep-ex] (PDF) ; CDS record ; inSPIRE record ; HepData record ; CADI line (restricted) ; | ||
| Figures | |
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Figure 1:
Feynman diagrams that contribute to ggF and VBF HH production at LO with coupling modifiers affecting the Higgs boson coupling strength to fermions ($ \kappa_f $), to vector bosons ($ \kappa_{\text{V}} $), to two vector boson vertices ($ \kappa_{\text{2V}} $), and the Higgs boson self-coupling strength ($ \kappa_{\lambda} $). |
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Figure 1-a:
Feynman diagrams that contribute to ggF and VBF HH production at LO with coupling modifiers affecting the Higgs boson coupling strength to fermions ($ \kappa_f $), to vector bosons ($ \kappa_{\text{V}} $), to two vector boson vertices ($ \kappa_{\text{2V}} $), and the Higgs boson self-coupling strength ($ \kappa_{\lambda} $). |
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Figure 1-b:
Feynman diagrams that contribute to ggF and VBF HH production at LO with coupling modifiers affecting the Higgs boson coupling strength to fermions ($ \kappa_f $), to vector bosons ($ \kappa_{\text{V}} $), to two vector boson vertices ($ \kappa_{\text{2V}} $), and the Higgs boson self-coupling strength ($ \kappa_{\lambda} $). |
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Figure 1-c:
Feynman diagrams that contribute to ggF and VBF HH production at LO with coupling modifiers affecting the Higgs boson coupling strength to fermions ($ \kappa_f $), to vector bosons ($ \kappa_{\text{V}} $), to two vector boson vertices ($ \kappa_{\text{2V}} $), and the Higgs boson self-coupling strength ($ \kappa_{\lambda} $). |
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Figure 1-d:
Feynman diagrams that contribute to ggF and VBF HH production at LO with coupling modifiers affecting the Higgs boson coupling strength to fermions ($ \kappa_f $), to vector bosons ($ \kappa_{\text{V}} $), to two vector boson vertices ($ \kappa_{\text{2V}} $), and the Higgs boson self-coupling strength ($ \kappa_{\lambda} $). |
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Figure 1-e:
Feynman diagrams that contribute to ggF and VBF HH production at LO with coupling modifiers affecting the Higgs boson coupling strength to fermions ($ \kappa_f $), to vector bosons ($ \kappa_{\text{V}} $), to two vector boson vertices ($ \kappa_{\text{2V}} $), and the Higgs boson self-coupling strength ($ \kappa_{\lambda} $). |
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Figure 2:
Left: the b tagging performance of the Run 3 PNET@HLT algorithm (in blue) compared to the best performing algorithm deployed at HLT in Run 2 (DEEPCSV, in red), as evaluated from $ \mathrm{t} \overline{\mathrm{t}} $ simulation on trigger-level AK4 jets with $ p_{\mathrm{T}}> $ 30 GeV and $ {|\eta| < 2.5} $. Right: efficiencies of the triggers targeting the resolved $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ topology as functions of the generator-level $ m_{\mathrm{H}\mathrm{H}} $ in simulated SM $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ signal events in which the generator-level jets from the b quarks produced by $ \mathrm{H} \to \mathrm{b}\overline{\mathrm{b}} $ decays have $ p_{\mathrm{T}} > $ 25 GeV and $ {|\eta| < 2.5} $. The teal histogram shows the expected distribution for the SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ signal at $ \sqrt{s}= $ 13.6 TeV prior to any trigger selections, scaled by an arbitrary constant for visibility. |
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Figure 2-a:
Left: the b tagging performance of the Run 3 PNET@HLT algorithm (in blue) compared to the best performing algorithm deployed at HLT in Run 2 (DEEPCSV, in red), as evaluated from $ \mathrm{t} \overline{\mathrm{t}} $ simulation on trigger-level AK4 jets with $ p_{\mathrm{T}}> $ 30 GeV and $ {|\eta| < 2.5} $. Right: efficiencies of the triggers targeting the resolved $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ topology as functions of the generator-level $ m_{\mathrm{H}\mathrm{H}} $ in simulated SM $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ signal events in which the generator-level jets from the b quarks produced by $ \mathrm{H} \to \mathrm{b}\overline{\mathrm{b}} $ decays have $ p_{\mathrm{T}} > $ 25 GeV and $ {|\eta| < 2.5} $. The teal histogram shows the expected distribution for the SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ signal at $ \sqrt{s}= $ 13.6 TeV prior to any trigger selections, scaled by an arbitrary constant for visibility. |
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Figure 2-b:
Left: the b tagging performance of the Run 3 PNET@HLT algorithm (in blue) compared to the best performing algorithm deployed at HLT in Run 2 (DEEPCSV, in red), as evaluated from $ \mathrm{t} \overline{\mathrm{t}} $ simulation on trigger-level AK4 jets with $ p_{\mathrm{T}}> $ 30 GeV and $ {|\eta| < 2.5} $. Right: efficiencies of the triggers targeting the resolved $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ topology as functions of the generator-level $ m_{\mathrm{H}\mathrm{H}} $ in simulated SM $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ signal events in which the generator-level jets from the b quarks produced by $ \mathrm{H} \to \mathrm{b}\overline{\mathrm{b}} $ decays have $ p_{\mathrm{T}} > $ 25 GeV and $ {|\eta| < 2.5} $. The teal histogram shows the expected distribution for the SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ signal at $ \sqrt{s}= $ 13.6 TeV prior to any trigger selections, scaled by an arbitrary constant for visibility. |
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Figure 3:
Left: the $ \mathrm{b}\overline{\mathrm{b}} $ tagging performance of the PNET@HLT algorithm (in blue) compared to the best performing Run 2 algorithm (DOUBLEBB, in red), as evaluated on AK8 jets in the HLT from simulated $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ and QCD multijet events with $ p_{\mathrm{T}} > $ 300 GeV and $ {|\eta| < 2.5} $. Right: efficiency of the logical or of the trigger paths developed for the merged topology, as a function of the generator-level leading H candidate $ p_{\mathrm{T}} $ in simulated SM $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ events in which $ {\Delta R(\mathrm{b},\overline{\mathrm{b}}) < 0.8} $. The teal histogram shows the expected distribution for the SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ signal at $ \sqrt{s}= $ 13.6 TeV prior to any selections, scaled by an arbitrary constant for visibility. |
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Figure 3-a:
Left: the $ \mathrm{b}\overline{\mathrm{b}} $ tagging performance of the PNET@HLT algorithm (in blue) compared to the best performing Run 2 algorithm (DOUBLEBB, in red), as evaluated on AK8 jets in the HLT from simulated $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ and QCD multijet events with $ p_{\mathrm{T}} > $ 300 GeV and $ {|\eta| < 2.5} $. Right: efficiency of the logical or of the trigger paths developed for the merged topology, as a function of the generator-level leading H candidate $ p_{\mathrm{T}} $ in simulated SM $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ events in which $ {\Delta R(\mathrm{b},\overline{\mathrm{b}}) < 0.8} $. The teal histogram shows the expected distribution for the SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ signal at $ \sqrt{s}= $ 13.6 TeV prior to any selections, scaled by an arbitrary constant for visibility. |
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Figure 3-b:
Left: the $ \mathrm{b}\overline{\mathrm{b}} $ tagging performance of the PNET@HLT algorithm (in blue) compared to the best performing Run 2 algorithm (DOUBLEBB, in red), as evaluated on AK8 jets in the HLT from simulated $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ and QCD multijet events with $ p_{\mathrm{T}} > $ 300 GeV and $ {|\eta| < 2.5} $. Right: efficiency of the logical or of the trigger paths developed for the merged topology, as a function of the generator-level leading H candidate $ p_{\mathrm{T}} $ in simulated SM $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ events in which $ {\Delta R(\mathrm{b},\overline{\mathrm{b}}) < 0.8} $. The teal histogram shows the expected distribution for the SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ signal at $ \sqrt{s}= $ 13.6 TeV prior to any selections, scaled by an arbitrary constant for visibility. |
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Figure 4:
Left: invariant mass distributions for the leading ($ m_{H_{1}} $) and subleading ($ m_{H_{2}} $) $ p_{\mathrm{T}} \mathrm{H} $ candidates in SM $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ events obtained before and after the application of the PNET jet $ p_{\mathrm{T}} $ regression. Right: distribution of the $ p_{\mathrm{T}} $ balance, $ r=p_{\mathrm{T}}(\mathrm{j_{1}})/p_{\mathrm{T}}(\mu\mu) $, in 2023 data and simulated events for the selected $ \mathrm{Z}(\mu\mu)+\mathrm{b}\text{ jet} $ region with $ \alpha=p_{\mathrm{T}}(\mathrm{j_{2}})/p_{\mathrm{T}}(\mu\mu) < $ 0.15, obtained after applying the PNET jet $ p_{\mathrm{T}} $ regression. The $ \mu $ values quoted in the legend correspond to the mean of the distributions. |
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Figure 4-a:
Left: invariant mass distributions for the leading ($ m_{H_{1}} $) and subleading ($ m_{H_{2}} $) $ p_{\mathrm{T}} \mathrm{H} $ candidates in SM $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ events obtained before and after the application of the PNET jet $ p_{\mathrm{T}} $ regression. Right: distribution of the $ p_{\mathrm{T}} $ balance, $ r=p_{\mathrm{T}}(\mathrm{j_{1}})/p_{\mathrm{T}}(\mu\mu) $, in 2023 data and simulated events for the selected $ \mathrm{Z}(\mu\mu)+\mathrm{b}\text{ jet} $ region with $ \alpha=p_{\mathrm{T}}(\mathrm{j_{2}})/p_{\mathrm{T}}(\mu\mu) < $ 0.15, obtained after applying the PNET jet $ p_{\mathrm{T}} $ regression. The $ \mu $ values quoted in the legend correspond to the mean of the distributions. |
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Figure 4-b:
Left: invariant mass distributions for the leading ($ m_{H_{1}} $) and subleading ($ m_{H_{2}} $) $ p_{\mathrm{T}} \mathrm{H} $ candidates in SM $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ events obtained before and after the application of the PNET jet $ p_{\mathrm{T}} $ regression. Right: distribution of the $ p_{\mathrm{T}} $ balance, $ r=p_{\mathrm{T}}(\mathrm{j_{1}})/p_{\mathrm{T}}(\mu\mu) $, in 2023 data and simulated events for the selected $ \mathrm{Z}(\mu\mu)+\mathrm{b}\text{ jet} $ region with $ \alpha=p_{\mathrm{T}}(\mathrm{j_{2}})/p_{\mathrm{T}}(\mu\mu) < $ 0.15, obtained after applying the PNET jet $ p_{\mathrm{T}} $ regression. The $ \mu $ values quoted in the legend correspond to the mean of the distributions. |
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Figure 5:
Left: the receiver operating characteristic curve for GLOPART and PNET for discriminating $ \mathrm{H} \to \mathrm{b}\overline{\mathrm{b}} $ from QCD jets with $ 400 < p_{\mathrm{T}} < $ 600 GeV, $ {|\eta| < 2.4} $, and $ 60 < m_{SD} < $ 150 GeV. The dotted and dash-dotted gray lines denote the signal efficiency at background efficiencies of 1 and 0.1%, respectively. Right: the mass regression performance for GLOPART and PNET for jets with $ p_{\mathrm{T}} > $ 200 GeV, $ {|\eta| < 2.4} $, and satisfying a PNET selection corresponding to 30% signal efficiency. The conditions correspond to those during data collection in 2023. |
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Figure 5-a:
Left: the receiver operating characteristic curve for GLOPART and PNET for discriminating $ \mathrm{H} \to \mathrm{b}\overline{\mathrm{b}} $ from QCD jets with $ 400 < p_{\mathrm{T}} < $ 600 GeV, $ {|\eta| < 2.4} $, and $ 60 < m_{SD} < $ 150 GeV. The dotted and dash-dotted gray lines denote the signal efficiency at background efficiencies of 1 and 0.1%, respectively. Right: the mass regression performance for GLOPART and PNET for jets with $ p_{\mathrm{T}} > $ 200 GeV, $ {|\eta| < 2.4} $, and satisfying a PNET selection corresponding to 30% signal efficiency. The conditions correspond to those during data collection in 2023. |
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Figure 5-b:
Left: the receiver operating characteristic curve for GLOPART and PNET for discriminating $ \mathrm{H} \to \mathrm{b}\overline{\mathrm{b}} $ from QCD jets with $ 400 < p_{\mathrm{T}} < $ 600 GeV, $ {|\eta| < 2.4} $, and $ 60 < m_{SD} < $ 150 GeV. The dotted and dash-dotted gray lines denote the signal efficiency at background efficiencies of 1 and 0.1%, respectively. Right: the mass regression performance for GLOPART and PNET for jets with $ p_{\mathrm{T}} > $ 200 GeV, $ {|\eta| < 2.4} $, and satisfying a PNET selection corresponding to 30% signal efficiency. The conditions correspond to those during data collection in 2023. |
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Figure 6:
Comparison between data and fit prediction from a simultaneous signal-plus-background fit to the GLOPART regressed jet mass distributions in $ \mathrm{Z} \to \mathrm{b}\overline{\mathrm{b}} $ VHP (left) and HP (middle) categories. Data corresponds to the full integrated luminosity collected with the CMS detector in 2022. The same comparison is performed for the $ m_{\mu\mu} $ distributions in the $ \mathrm{Z} \to \mu\mu $ region (right). Each process considered in the fit is modeled via a parametric function, as described in the text. |
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Figure 6-a:
Comparison between data and fit prediction from a simultaneous signal-plus-background fit to the GLOPART regressed jet mass distributions in $ \mathrm{Z} \to \mathrm{b}\overline{\mathrm{b}} $ VHP (left) and HP (middle) categories. Data corresponds to the full integrated luminosity collected with the CMS detector in 2022. The same comparison is performed for the $ m_{\mu\mu} $ distributions in the $ \mathrm{Z} \to \mu\mu $ region (right). Each process considered in the fit is modeled via a parametric function, as described in the text. |
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Figure 6-b:
Comparison between data and fit prediction from a simultaneous signal-plus-background fit to the GLOPART regressed jet mass distributions in $ \mathrm{Z} \to \mathrm{b}\overline{\mathrm{b}} $ VHP (left) and HP (middle) categories. Data corresponds to the full integrated luminosity collected with the CMS detector in 2022. The same comparison is performed for the $ m_{\mu\mu} $ distributions in the $ \mathrm{Z} \to \mu\mu $ region (right). Each process considered in the fit is modeled via a parametric function, as described in the text. |
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Figure 6-c:
Comparison between data and fit prediction from a simultaneous signal-plus-background fit to the GLOPART regressed jet mass distributions in $ \mathrm{Z} \to \mathrm{b}\overline{\mathrm{b}} $ VHP (left) and HP (middle) categories. Data corresponds to the full integrated luminosity collected with the CMS detector in 2022. The same comparison is performed for the $ m_{\mu\mu} $ distributions in the $ \mathrm{Z} \to \mu\mu $ region (right). Each process considered in the fit is modeled via a parametric function, as described in the text. |
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Figure 7:
Left: schematic diagram of the signal regions (dark shaded circles) and control regions (annular regions) in the $ m_{{H}_{1}} $-- $ m_{{H}_{2}} $ mass plane as a function of $ N_{\mathrm{bjet}} $. Right: schematic diagram showing the background estimation strategy, which applies a multidimensional reweighting of events from $ \mathrm{SR_{2\mathrm{b}}} $ to $ \mathrm{SR_{4\mathrm{b}}} $. |
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Figure 7-a:
Left: schematic diagram of the signal regions (dark shaded circles) and control regions (annular regions) in the $ m_{{H}_{1}} $-- $ m_{{H}_{2}} $ mass plane as a function of $ N_{\mathrm{bjet}} $. Right: schematic diagram showing the background estimation strategy, which applies a multidimensional reweighting of events from $ \mathrm{SR_{2\mathrm{b}}} $ to $ \mathrm{SR_{4\mathrm{b}}} $. |
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Figure 7-b:
Left: schematic diagram of the signal regions (dark shaded circles) and control regions (annular regions) in the $ m_{{H}_{1}} $-- $ m_{{H}_{2}} $ mass plane as a function of $ N_{\mathrm{bjet}} $. Right: schematic diagram showing the background estimation strategy, which applies a multidimensional reweighting of events from $ \mathrm{SR_{2\mathrm{b}}} $ to $ \mathrm{SR_{4\mathrm{b}}} $. |
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Figure 8:
Distribution of the SvsB classifier score in the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ category for $ \mathrm{SR_{3\mathrm{b}}} $ data (black points) compared to the background prediction (blue histogram), for the pre-ParkingHH (left) and post-ParkingHH (right) data sets. The middle panel shows the ratio of data to the pre-fit (red open markers) and post background-only fit (black solid markers) background prediction, with the gray band indicating the background post-fit uncertainty. The lower panel shows the distribution of the pulls, defined as the difference between the data and the post-fit background prediction, divided by the statistical uncertainty in the data. |
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Figure 8-a:
Distribution of the SvsB classifier score in the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ category for $ \mathrm{SR_{3\mathrm{b}}} $ data (black points) compared to the background prediction (blue histogram), for the pre-ParkingHH (left) and post-ParkingHH (right) data sets. The middle panel shows the ratio of data to the pre-fit (red open markers) and post background-only fit (black solid markers) background prediction, with the gray band indicating the background post-fit uncertainty. The lower panel shows the distribution of the pulls, defined as the difference between the data and the post-fit background prediction, divided by the statistical uncertainty in the data. |
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Figure 8-b:
Distribution of the SvsB classifier score in the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ category for $ \mathrm{SR_{3\mathrm{b}}} $ data (black points) compared to the background prediction (blue histogram), for the pre-ParkingHH (left) and post-ParkingHH (right) data sets. The middle panel shows the ratio of data to the pre-fit (red open markers) and post background-only fit (black solid markers) background prediction, with the gray band indicating the background post-fit uncertainty. The lower panel shows the distribution of the pulls, defined as the difference between the data and the post-fit background prediction, divided by the statistical uncertainty in the data. |
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Figure 9:
A schematic diagram of the 4b validation regions defined in the $ m_{{H}_{1}} $-- $ m_{{H}_{2}} $mass plane and orthogonal to the $ \mathrm{SR_{4\mathrm{b}}} $. In each validation region, the solid blue area identifies the signal region, while the dashed blue lines indicate the corresponding control region. The ``leading'' H candidate is the one with largest $ p_{\mathrm{T}}(\mathrm{H}) $. |
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Figure 10:
Pre-fit and post background-only fit distributions of the SvsB classifier output for the sum of all 4b validation regions in pre-ParkingHH (left) and post-ParkingHH (right) data. Notations are as in Fig. 8. |
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Figure 10-a:
Pre-fit and post background-only fit distributions of the SvsB classifier output for the sum of all 4b validation regions in pre-ParkingHH (left) and post-ParkingHH (right) data. Notations are as in Fig. 8. |
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Figure 10-b:
Pre-fit and post background-only fit distributions of the SvsB classifier output for the sum of all 4b validation regions in pre-ParkingHH (left) and post-ParkingHH (right) data. Notations are as in Fig. 8. |
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Figure 11:
Post-fit distributions of the SvsB classifier score in the $ \mathrm{SR_{4\mathrm{b}}} $ of the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ resolved analysis for data (black points) and the predicted background (blue filled histograms), for pre-ParkingHH (left) and post-ParkingHH (right) data. The distributions of the SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ (orange line) and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ (red line) signals, scaled to improve their visibility, are overlaid. Notations are as in Fig. 8. |
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Figure 11-a:
Post-fit distributions of the SvsB classifier score in the $ \mathrm{SR_{4\mathrm{b}}} $ of the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ resolved analysis for data (black points) and the predicted background (blue filled histograms), for pre-ParkingHH (left) and post-ParkingHH (right) data. The distributions of the SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ (orange line) and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ (red line) signals, scaled to improve their visibility, are overlaid. Notations are as in Fig. 8. |
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Figure 11-b:
Post-fit distributions of the SvsB classifier score in the $ \mathrm{SR_{4\mathrm{b}}} $ of the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ resolved analysis for data (black points) and the predicted background (blue filled histograms), for pre-ParkingHH (left) and post-ParkingHH (right) data. The distributions of the SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ (orange line) and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ (red line) signals, scaled to improve their visibility, are overlaid. Notations are as in Fig. 8. |
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Figure 12:
Post-fit distributions of the SvsB output score in the $ \mathrm{SR_{4\mathrm{b}}} $ of the $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ resolved analysis reported for data (black points) and the predicted background (blue filled histograms) for pre-ParkingHH (left) and post-ParkingHH (right) data. The distributions of the SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ (orange line) and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $(red line) signals, scaled to improve their visibility, are overlaid. Notations are as in Fig. 8. |
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Figure 12-a:
Post-fit distributions of the SvsB output score in the $ \mathrm{SR_{4\mathrm{b}}} $ of the $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ resolved analysis reported for data (black points) and the predicted background (blue filled histograms) for pre-ParkingHH (left) and post-ParkingHH (right) data. The distributions of the SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ (orange line) and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $(red line) signals, scaled to improve their visibility, are overlaid. Notations are as in Fig. 8. |
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Figure 12-b:
Post-fit distributions of the SvsB output score in the $ \mathrm{SR_{4\mathrm{b}}} $ of the $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ resolved analysis reported for data (black points) and the predicted background (blue filled histograms) for pre-ParkingHH (left) and post-ParkingHH (right) data. The distributions of the SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ (orange line) and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $(red line) signals, scaled to improve their visibility, are overlaid. Notations are as in Fig. 8. |
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Figure 13:
The expected HH, ZH, and ZZ signal yields as estimated from simulation (left) and the observed data (right) for the Run 3 data set, in the 3T1M region, as a function of the reconstructed masses of the leading and subleading in $ p_{\mathrm{T}} \mathrm{H} $ candidates. The signal region is defined by the union of the regions enclosed by the dashed red lines. |
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Figure 13-a:
The expected HH, ZH, and ZZ signal yields as estimated from simulation (left) and the observed data (right) for the Run 3 data set, in the 3T1M region, as a function of the reconstructed masses of the leading and subleading in $ p_{\mathrm{T}} \mathrm{H} $ candidates. The signal region is defined by the union of the regions enclosed by the dashed red lines. |
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Figure 13-b:
The expected HH, ZH, and ZZ signal yields as estimated from simulation (left) and the observed data (right) for the Run 3 data set, in the 3T1M region, as a function of the reconstructed masses of the leading and subleading in $ p_{\mathrm{T}} \mathrm{H} $ candidates. The signal region is defined by the union of the regions enclosed by the dashed red lines. |
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Figure 14:
Distribution of the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $, $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $, ZZ, and ZH signal processes, normalized to unity, as a function of the three FEYNNET probability scores. |
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Figure 15:
Post-fit distribution of the $\mathcal{D}(\mathrm{ggHH-vs-bkg})$ (upper left), $\mathcal{D}(\mathrm{qqHH-vs-bkg})$ (upper right), $\mathcal{D}(\mathrm{ZZ-vs-bkg})$ (lower left), and $\mathcal{D}(\mathrm{ZH-vs-bkg})$ (lower right) scores in the validation region $ \text{SR}_{\text{3MnT}} $ for data (black points) and the predicted background (gray filled histograms) with the Run 3 data set. Notations are as in Fig. 8. |
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Figure 15-a:
Post-fit distribution of the $\mathcal{D}(\mathrm{ggHH-vs-bkg})$ (upper left), $\mathcal{D}(\mathrm{qqHH-vs-bkg})$ (upper right), $\mathcal{D}(\mathrm{ZZ-vs-bkg})$ (lower left), and $\mathcal{D}(\mathrm{ZH-vs-bkg})$ (lower right) scores in the validation region $ \text{SR}_{\text{3MnT}} $ for data (black points) and the predicted background (gray filled histograms) with the Run 3 data set. Notations are as in Fig. 8. |
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Figure 15-b:
Post-fit distribution of the $\mathcal{D}(\mathrm{ggHH-vs-bkg})$ (upper left), $\mathcal{D}(\mathrm{qqHH-vs-bkg})$ (upper right), $\mathcal{D}(\mathrm{ZZ-vs-bkg})$ (lower left), and $\mathcal{D}(\mathrm{ZH-vs-bkg})$ (lower right) scores in the validation region $ \text{SR}_{\text{3MnT}} $ for data (black points) and the predicted background (gray filled histograms) with the Run 3 data set. Notations are as in Fig. 8. |
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Figure 15-c:
Post-fit distribution of the $\mathcal{D}(\mathrm{ggHH-vs-bkg})$ (upper left), $\mathcal{D}(\mathrm{qqHH-vs-bkg})$ (upper right), $\mathcal{D}(\mathrm{ZZ-vs-bkg})$ (lower left), and $\mathcal{D}(\mathrm{ZH-vs-bkg})$ (lower right) scores in the validation region $ \text{SR}_{\text{3MnT}} $ for data (black points) and the predicted background (gray filled histograms) with the Run 3 data set. Notations are as in Fig. 8. |
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Figure 15-d:
Post-fit distribution of the $\mathcal{D}(\mathrm{ggHH-vs-bkg})$ (upper left), $\mathcal{D}(\mathrm{qqHH-vs-bkg})$ (upper right), $\mathcal{D}(\mathrm{ZZ-vs-bkg})$ (lower left), and $\mathcal{D}(\mathrm{ZH-vs-bkg})$ (lower right) scores in the validation region $ \text{SR}_{\text{3MnT}} $ for data (black points) and the predicted background (gray filled histograms) with the Run 3 data set. Notations are as in Fig. 8. |
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Figure 16:
Post-fit distributions of the transformed $\mathcal{D}(\mathrm{ggHH-vs-bkg})$ score in the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} \text{SR}_{\text{3T1M}} $ (left) and $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} \text{SR}_{\text{3T1L}} $ (middle) categories, and $\mathcal{D}(\mathrm{qqHH-vs-bkg})$ score in the $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} \text{SR}_{\text{3T1M}} $ (right) category for data (black points) and the predicted background (gray filled histograms) for the Run 3 data set. The distributions of the SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ (orange line) and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ (red line) signal processes, scaled to improve their visibility, are also overlaid. Notations are as in Fig. 8. |
png pdf |
Figure 16-a:
Post-fit distributions of the transformed $\mathcal{D}(\mathrm{ggHH-vs-bkg})$ score in the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} \text{SR}_{\text{3T1M}} $ (left) and $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} \text{SR}_{\text{3T1L}} $ (middle) categories, and $\mathcal{D}(\mathrm{qqHH-vs-bkg})$ score in the $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} \text{SR}_{\text{3T1M}} $ (right) category for data (black points) and the predicted background (gray filled histograms) for the Run 3 data set. The distributions of the SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ (orange line) and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ (red line) signal processes, scaled to improve their visibility, are also overlaid. Notations are as in Fig. 8. |
png pdf |
Figure 16-b:
Post-fit distributions of the transformed $\mathcal{D}(\mathrm{ggHH-vs-bkg})$ score in the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} \text{SR}_{\text{3T1M}} $ (left) and $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} \text{SR}_{\text{3T1L}} $ (middle) categories, and $\mathcal{D}(\mathrm{qqHH-vs-bkg})$ score in the $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} \text{SR}_{\text{3T1M}} $ (right) category for data (black points) and the predicted background (gray filled histograms) for the Run 3 data set. The distributions of the SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ (orange line) and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ (red line) signal processes, scaled to improve their visibility, are also overlaid. Notations are as in Fig. 8. |
png pdf |
Figure 16-c:
Post-fit distributions of the transformed $\mathcal{D}(\mathrm{ggHH-vs-bkg})$ score in the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} \text{SR}_{\text{3T1M}} $ (left) and $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} \text{SR}_{\text{3T1L}} $ (middle) categories, and $\mathcal{D}(\mathrm{qqHH-vs-bkg})$ score in the $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} \text{SR}_{\text{3T1M}} $ (right) category for data (black points) and the predicted background (gray filled histograms) for the Run 3 data set. The distributions of the SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ (orange line) and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ (red line) signal processes, scaled to improve their visibility, are also overlaid. Notations are as in Fig. 8. |
png pdf |
Figure 17:
Post-fit distribution of the transformed $\mathcal{D}(\mathrm{ZZ-vs-bkg})$ score in the $ \mathrm{Z}\mathrm{Z} \text{SR}_{\text{3T1M}} $ (upper left) and $ \mathrm{Z}\mathrm{Z} \text{SR}_{\text{3T1L}} $ (upper right) categories, and $\mathcal{D}(\mathrm{ZH-vs-bkg})$ score in the $ \mathrm{Z}\mathrm{H} \text{SR}_{\text{3T1M}} $ (lower left) and $ \mathrm{Z}\mathrm{H} \text{SR}_{\text{3T1L}} $ (lower right) SR for data (black points) and the predicted background (gray filled histograms) for the Run 3 data set. The distributions of the SM ZZ (green line) and ZH (purple line) processes are also overlaid. Notations are as in Fig. 8. |
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Figure 17-a:
Post-fit distribution of the transformed $\mathcal{D}(\mathrm{ZZ-vs-bkg})$ score in the $ \mathrm{Z}\mathrm{Z} \text{SR}_{\text{3T1M}} $ (upper left) and $ \mathrm{Z}\mathrm{Z} \text{SR}_{\text{3T1L}} $ (upper right) categories, and $\mathcal{D}(\mathrm{ZH-vs-bkg})$ score in the $ \mathrm{Z}\mathrm{H} \text{SR}_{\text{3T1M}} $ (lower left) and $ \mathrm{Z}\mathrm{H} \text{SR}_{\text{3T1L}} $ (lower right) SR for data (black points) and the predicted background (gray filled histograms) for the Run 3 data set. The distributions of the SM ZZ (green line) and ZH (purple line) processes are also overlaid. Notations are as in Fig. 8. |
png pdf |
Figure 17-b:
Post-fit distribution of the transformed $\mathcal{D}(\mathrm{ZZ-vs-bkg})$ score in the $ \mathrm{Z}\mathrm{Z} \text{SR}_{\text{3T1M}} $ (upper left) and $ \mathrm{Z}\mathrm{Z} \text{SR}_{\text{3T1L}} $ (upper right) categories, and $\mathcal{D}(\mathrm{ZH-vs-bkg})$ score in the $ \mathrm{Z}\mathrm{H} \text{SR}_{\text{3T1M}} $ (lower left) and $ \mathrm{Z}\mathrm{H} \text{SR}_{\text{3T1L}} $ (lower right) SR for data (black points) and the predicted background (gray filled histograms) for the Run 3 data set. The distributions of the SM ZZ (green line) and ZH (purple line) processes are also overlaid. Notations are as in Fig. 8. |
png pdf |
Figure 17-c:
Post-fit distribution of the transformed $\mathcal{D}(\mathrm{ZZ-vs-bkg})$ score in the $ \mathrm{Z}\mathrm{Z} \text{SR}_{\text{3T1M}} $ (upper left) and $ \mathrm{Z}\mathrm{Z} \text{SR}_{\text{3T1L}} $ (upper right) categories, and $\mathcal{D}(\mathrm{ZH-vs-bkg})$ score in the $ \mathrm{Z}\mathrm{H} \text{SR}_{\text{3T1M}} $ (lower left) and $ \mathrm{Z}\mathrm{H} \text{SR}_{\text{3T1L}} $ (lower right) SR for data (black points) and the predicted background (gray filled histograms) for the Run 3 data set. The distributions of the SM ZZ (green line) and ZH (purple line) processes are also overlaid. Notations are as in Fig. 8. |
png pdf |
Figure 17-d:
Post-fit distribution of the transformed $\mathcal{D}(\mathrm{ZZ-vs-bkg})$ score in the $ \mathrm{Z}\mathrm{Z} \text{SR}_{\text{3T1M}} $ (upper left) and $ \mathrm{Z}\mathrm{Z} \text{SR}_{\text{3T1L}} $ (upper right) categories, and $\mathcal{D}(\mathrm{ZH-vs-bkg})$ score in the $ \mathrm{Z}\mathrm{H} \text{SR}_{\text{3T1M}} $ (lower left) and $ \mathrm{Z}\mathrm{H} \text{SR}_{\text{3T1L}} $ (lower right) SR for data (black points) and the predicted background (gray filled histograms) for the Run 3 data set. The distributions of the SM ZZ (green line) and ZH (purple line) processes are also overlaid. Notations are as in Fig. 8. |
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Figure 18:
The fitted signal+background distribution of the signal probability, $ \mathcal{P}_{\text{HH}} $, in the $ \mathrm{H}\mathrm{H} \mathrm{SR_{4\mathrm{b}}} $. The black points show the 4b events from data. The yellow and blue regions show the predictions from the QCD multijet model and the $ \mathrm{t} \overline{\mathrm{t}} $ simulation, respectively. The prediction for the SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ signal distribution is given by the red histogram, multiplied by 100. The lower panel shows the data-to-background ratio, with the hatched area representing the background uncertainty. |
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Figure 19:
Left: schematic diagram showing the regions used for the background estimation strategy used in the merged channel. The purity in QCD multijet events is reported in each of the background-enriched regions ($ \mathrm{CR_{QCD,A}} $, $ \mathrm{CR_{QCD,B}} $, and $ \mathrm{CR_{QCD,C}} $). Right: schematic diagram of the signal (orange area) and CRs (QCD in green, $ \mathrm{t} \overline{\mathrm{t}} $ in azure) in the plane defined by the $ m_\text{reg}(H_1) $ and $ m_\text{reg}(H_2) $. |
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Figure 19-a:
Left: schematic diagram showing the regions used for the background estimation strategy used in the merged channel. The purity in QCD multijet events is reported in each of the background-enriched regions ($ \mathrm{CR_{QCD,A}} $, $ \mathrm{CR_{QCD,B}} $, and $ \mathrm{CR_{QCD,C}} $). Right: schematic diagram of the signal (orange area) and CRs (QCD in green, $ \mathrm{t} \overline{\mathrm{t}} $ in azure) in the plane defined by the $ m_\text{reg}(H_1) $ and $ m_\text{reg}(H_2) $. |
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Figure 19-b:
Left: schematic diagram showing the regions used for the background estimation strategy used in the merged channel. The purity in QCD multijet events is reported in each of the background-enriched regions ($ \mathrm{CR_{QCD,A}} $, $ \mathrm{CR_{QCD,B}} $, and $ \mathrm{CR_{QCD,C}} $). Right: schematic diagram of the signal (orange area) and CRs (QCD in green, $ \mathrm{t} \overline{\mathrm{t}} $ in azure) in the plane defined by the $ m_\text{reg}(H_1) $ and $ m_\text{reg}(H_2) $. |
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Figure 20:
Pre-fit and post-fit distributions of the fitted observables for the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $-inclusive HPSR (left), LPSR (center), and VR (right) categories of the merged analysis following the mass fit method. Distributions are shown for data (black points) and the different background contributions from QCD multijet, $ \mathrm{t} \overline{\mathrm{t}} $, $ \mathrm{V}+\text{jets} $, VV, and ZH productions. The expected distributions for SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ (orange) and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ (red) signals are overlaid and scaled by a multiplicative factor to improve their visibility. Notations are as in Fig. 8. |
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Figure 20-a:
Pre-fit and post-fit distributions of the fitted observables for the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $-inclusive HPSR (left), LPSR (center), and VR (right) categories of the merged analysis following the mass fit method. Distributions are shown for data (black points) and the different background contributions from QCD multijet, $ \mathrm{t} \overline{\mathrm{t}} $, $ \mathrm{V}+\text{jets} $, VV, and ZH productions. The expected distributions for SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ (orange) and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ (red) signals are overlaid and scaled by a multiplicative factor to improve their visibility. Notations are as in Fig. 8. |
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Figure 20-b:
Pre-fit and post-fit distributions of the fitted observables for the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $-inclusive HPSR (left), LPSR (center), and VR (right) categories of the merged analysis following the mass fit method. Distributions are shown for data (black points) and the different background contributions from QCD multijet, $ \mathrm{t} \overline{\mathrm{t}} $, $ \mathrm{V}+\text{jets} $, VV, and ZH productions. The expected distributions for SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ (orange) and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ (red) signals are overlaid and scaled by a multiplicative factor to improve their visibility. Notations are as in Fig. 8. |
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Figure 20-c:
Pre-fit and post-fit distributions of the fitted observables for the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $-inclusive HPSR (left), LPSR (center), and VR (right) categories of the merged analysis following the mass fit method. Distributions are shown for data (black points) and the different background contributions from QCD multijet, $ \mathrm{t} \overline{\mathrm{t}} $, $ \mathrm{V}+\text{jets} $, VV, and ZH productions. The expected distributions for SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ (orange) and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ (red) signals are overlaid and scaled by a multiplicative factor to improve their visibility. Notations are as in Fig. 8. |
png pdf |
Figure 21:
Pre-fit and post-fit distributions of the fitted observables for the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $-inclusive HPSR (left), LPSR (center), and VR (right) categories of the merged analysis following the DNN fit method. Distributions are shown for data (black points) and the different background contributions from QCD multijet, $ \mathrm{t} \overline{\mathrm{t}} $, $ \mathrm{V}+\text{jets} $, VV, and ZH productions. The expected distributions for SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ (orange) and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ (red) signals are overlaid and scaled by a multiplicative factor to improve their visibility. Notations are as in Fig. 8. |
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Figure 21-a:
Pre-fit and post-fit distributions of the fitted observables for the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $-inclusive HPSR (left), LPSR (center), and VR (right) categories of the merged analysis following the DNN fit method. Distributions are shown for data (black points) and the different background contributions from QCD multijet, $ \mathrm{t} \overline{\mathrm{t}} $, $ \mathrm{V}+\text{jets} $, VV, and ZH productions. The expected distributions for SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ (orange) and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ (red) signals are overlaid and scaled by a multiplicative factor to improve their visibility. Notations are as in Fig. 8. |
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Figure 21-b:
Pre-fit and post-fit distributions of the fitted observables for the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $-inclusive HPSR (left), LPSR (center), and VR (right) categories of the merged analysis following the DNN fit method. Distributions are shown for data (black points) and the different background contributions from QCD multijet, $ \mathrm{t} \overline{\mathrm{t}} $, $ \mathrm{V}+\text{jets} $, VV, and ZH productions. The expected distributions for SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ (orange) and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ (red) signals are overlaid and scaled by a multiplicative factor to improve their visibility. Notations are as in Fig. 8. |
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Figure 21-c:
Pre-fit and post-fit distributions of the fitted observables for the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $-inclusive HPSR (left), LPSR (center), and VR (right) categories of the merged analysis following the DNN fit method. Distributions are shown for data (black points) and the different background contributions from QCD multijet, $ \mathrm{t} \overline{\mathrm{t}} $, $ \mathrm{V}+\text{jets} $, VV, and ZH productions. The expected distributions for SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ (orange) and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ (red) signals are overlaid and scaled by a multiplicative factor to improve their visibility. Notations are as in Fig. 8. |
png pdf |
Figure 22:
Pre-fit and post-fit distributions of the fitted observables for the $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ HPSR (left) and LPSR (right) categories of the merged analysis. Distributions are shown for data (black points) and the different background contributions from QCD multijet, $ \mathrm{t} \overline{\mathrm{t}} $, $ \mathrm{V}+\text{jets} $, VV, and ZH productions. The expected distributions for SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ (orange) and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ (red) signals are overlaid and scaled by a multiplicative factor to improve their visibility. Notations are as in Fig. 8. |
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Figure 22-a:
Pre-fit and post-fit distributions of the fitted observables for the $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ HPSR (left) and LPSR (right) categories of the merged analysis. Distributions are shown for data (black points) and the different background contributions from QCD multijet, $ \mathrm{t} \overline{\mathrm{t}} $, $ \mathrm{V}+\text{jets} $, VV, and ZH productions. The expected distributions for SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ (orange) and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ (red) signals are overlaid and scaled by a multiplicative factor to improve their visibility. Notations are as in Fig. 8. |
png pdf |
Figure 22-b:
Pre-fit and post-fit distributions of the fitted observables for the $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ HPSR (left) and LPSR (right) categories of the merged analysis. Distributions are shown for data (black points) and the different background contributions from QCD multijet, $ \mathrm{t} \overline{\mathrm{t}} $, $ \mathrm{V}+\text{jets} $, VV, and ZH productions. The expected distributions for SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ (orange) and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ (red) signals are overlaid and scaled by a multiplicative factor to improve their visibility. Notations are as in Fig. 8. |
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Figure 23:
Schematic diagrams showing the SRs and QCD CR (gray) used in the merged analysis described in Section 6.3. Left: the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ SR 1 (red), 2 (blue), and 3 (orange) are defined based on successively lower selections on the $ T_{\text{X}\mathrm{b}\overline{\mathrm{b}}} $ score of the \HepParticleHu candidate and the $ \mathcal{D} $ ($ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $-vs-bkg) score. Right: the $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ SR (purple) is defined based on a tight selection on $ \mathcal{D} $ ($ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $-vs-bkg) and a loose selection on $ T_{\text{X}\mathrm{b}\overline{\mathrm{b}}} $ of the \HepParticleHu candidate. |
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Figure 24:
The background-only fit distributions of the regressed mass of the subleading H candidate $ m_\text{reg}(H_2) $ in $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ SR 1 (upper left), $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ SR (upper right), $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ SR 2 (lower left), and $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ SR 3 (lower right). The SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ signal are overlaid in the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ SRs scaled by different factors. The targeted $ \kappa_{\text{2V}}= $ 0 $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ signal is also overlaid in the $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ SR. Notations are as in Fig. 8. |
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Figure 24-a:
The background-only fit distributions of the regressed mass of the subleading H candidate $ m_\text{reg}(H_2) $ in $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ SR 1 (upper left), $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ SR (upper right), $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ SR 2 (lower left), and $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ SR 3 (lower right). The SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ signal are overlaid in the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ SRs scaled by different factors. The targeted $ \kappa_{\text{2V}}= $ 0 $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ signal is also overlaid in the $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ SR. Notations are as in Fig. 8. |
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Figure 24-b:
The background-only fit distributions of the regressed mass of the subleading H candidate $ m_\text{reg}(H_2) $ in $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ SR 1 (upper left), $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ SR (upper right), $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ SR 2 (lower left), and $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ SR 3 (lower right). The SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ signal are overlaid in the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ SRs scaled by different factors. The targeted $ \kappa_{\text{2V}}= $ 0 $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ signal is also overlaid in the $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ SR. Notations are as in Fig. 8. |
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Figure 24-c:
The background-only fit distributions of the regressed mass of the subleading H candidate $ m_\text{reg}(H_2) $ in $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ SR 1 (upper left), $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ SR (upper right), $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ SR 2 (lower left), and $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ SR 3 (lower right). The SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ signal are overlaid in the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ SRs scaled by different factors. The targeted $ \kappa_{\text{2V}}= $ 0 $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ signal is also overlaid in the $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ SR. Notations are as in Fig. 8. |
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Figure 24-d:
The background-only fit distributions of the regressed mass of the subleading H candidate $ m_\text{reg}(H_2) $ in $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ SR 1 (upper left), $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ SR (upper right), $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ SR 2 (lower left), and $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ SR 3 (lower right). The SM $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ signal are overlaid in the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $ SRs scaled by different factors. The targeted $ \kappa_{\text{2V}}= $ 0 $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ signal is also overlaid in the $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ SR. Notations are as in Fig. 8. |
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Figure 25:
Left: the observed (solid) and expected (dashed) 95% CL upper limits on the signal strength of HH production from the resolved analysis with overlap removed, the merged HPSR mass fit category, and their combination. The orange and blue bands represent, respectively, the 68 and 95% CL intervals around the expected limit. Right: the same breakdown of 95% CL upper limits on the signal strength of $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ production. |
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Figure 25-a:
Left: the observed (solid) and expected (dashed) 95% CL upper limits on the signal strength of HH production from the resolved analysis with overlap removed, the merged HPSR mass fit category, and their combination. The orange and blue bands represent, respectively, the 68 and 95% CL intervals around the expected limit. Right: the same breakdown of 95% CL upper limits on the signal strength of $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ production. |
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Figure 25-b:
Left: the observed (solid) and expected (dashed) 95% CL upper limits on the signal strength of HH production from the resolved analysis with overlap removed, the merged HPSR mass fit category, and their combination. The orange and blue bands represent, respectively, the 68 and 95% CL intervals around the expected limit. Right: the same breakdown of 95% CL upper limits on the signal strength of $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ production. |
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Figure 26:
The observed (solid) and expected (dashed) 95% CL upper limits on the signal strength of the HH production ($ \mu_{\mathrm{H}\mathrm{H}} $) obtained as functions of $ \kappa_{\lambda} $ (left) and $ \kappa_{\text{2V}} $ (right) for the combined fit of resolved and merged $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ analyses. The orange and blue bands represent, respectively, the 68 and 95% CL intervals around the expected limit. The horizontal red lines indicate the SM prediction. |
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Figure 26-a:
The observed (solid) and expected (dashed) 95% CL upper limits on the signal strength of the HH production ($ \mu_{\mathrm{H}\mathrm{H}} $) obtained as functions of $ \kappa_{\lambda} $ (left) and $ \kappa_{\text{2V}} $ (right) for the combined fit of resolved and merged $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ analyses. The orange and blue bands represent, respectively, the 68 and 95% CL intervals around the expected limit. The horizontal red lines indicate the SM prediction. |
png pdf |
Figure 26-b:
The observed (solid) and expected (dashed) 95% CL upper limits on the signal strength of the HH production ($ \mu_{\mathrm{H}\mathrm{H}} $) obtained as functions of $ \kappa_{\lambda} $ (left) and $ \kappa_{\text{2V}} $ (right) for the combined fit of resolved and merged $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ analyses. The orange and blue bands represent, respectively, the 68 and 95% CL intervals around the expected limit. The horizontal red lines indicate the SM prediction. |
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Figure 27:
The observed (solid) and expected (dashed) profile likelihood ratios as function of $ \kappa_{\lambda} $ (left) and $ \kappa_{\text{2V}} $ (right) for the combined fit of resolved and merged $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ analyses, where the expected is obtained from an Asimov dataset [111] defined by fixing the nuisance parameters to values obtained from a fit to data with $ {\mu_{\mathrm{H}\mathrm{H}}=1} $. The $ {q_{\kappa} < 1} $ and $ {q_{\kappa} < 3.84} $ levels are indicated with the dashed red lines. |
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Figure 27-a:
The observed (solid) and expected (dashed) profile likelihood ratios as function of $ \kappa_{\lambda} $ (left) and $ \kappa_{\text{2V}} $ (right) for the combined fit of resolved and merged $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ analyses, where the expected is obtained from an Asimov dataset [111] defined by fixing the nuisance parameters to values obtained from a fit to data with $ {\mu_{\mathrm{H}\mathrm{H}}=1} $. The $ {q_{\kappa} < 1} $ and $ {q_{\kappa} < 3.84} $ levels are indicated with the dashed red lines. |
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Figure 27-b:
The observed (solid) and expected (dashed) profile likelihood ratios as function of $ \kappa_{\lambda} $ (left) and $ \kappa_{\text{2V}} $ (right) for the combined fit of resolved and merged $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ analyses, where the expected is obtained from an Asimov dataset [111] defined by fixing the nuisance parameters to values obtained from a fit to data with $ {\mu_{\mathrm{H}\mathrm{H}}=1} $. The $ {q_{\kappa} < 1} $ and $ {q_{\kappa} < 3.84} $ levels are indicated with the dashed red lines. |
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Figure 28:
The observed and expected 2D exclusion range for ($ \mu_{\mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H}} $, $ \mu_{\mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H}} $) (left) and ($ \kappa_{\lambda} $,$ \kappa_{\text{2V}} $) (right) for the combined fit of resolved and merged $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ analyses. The black solid and dashed lines show the observed exclusion contours for $ {q_{\kappa} < 2.30} $ and $ {q_{\kappa} < 5.99} $, respectively, while the orange and blue shaded regions show the expected exclusion contours. The red circle indicates the SM prediction, while the black cross shows the best fit result. |
png pdf |
Figure 28-a:
The observed and expected 2D exclusion range for ($ \mu_{\mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H}} $, $ \mu_{\mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H}} $) (left) and ($ \kappa_{\lambda} $,$ \kappa_{\text{2V}} $) (right) for the combined fit of resolved and merged $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ analyses. The black solid and dashed lines show the observed exclusion contours for $ {q_{\kappa} < 2.30} $ and $ {q_{\kappa} < 5.99} $, respectively, while the orange and blue shaded regions show the expected exclusion contours. The red circle indicates the SM prediction, while the black cross shows the best fit result. |
png pdf |
Figure 28-b:
The observed and expected 2D exclusion range for ($ \mu_{\mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H}} $, $ \mu_{\mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H}} $) (left) and ($ \kappa_{\lambda} $,$ \kappa_{\text{2V}} $) (right) for the combined fit of resolved and merged $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ analyses. The black solid and dashed lines show the observed exclusion contours for $ {q_{\kappa} < 2.30} $ and $ {q_{\kappa} < 5.99} $, respectively, while the orange and blue shaded regions show the expected exclusion contours. The red circle indicates the SM prediction, while the black cross shows the best fit result. |
png pdf |
Figure 29:
Left: the observed (solid) and expected (dashed) 95% CL upper limits on the signal strength of HH production from the combination of Run 2 and Run 3 analyses and for each data set separately. The orange and blue bands represent, respectively, the 68 and 95% CL intervals around the expected limit. Right: the same breakdown of 95% CL upper limits on the signal strength of $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ production. |
png pdf |
Figure 29-a:
Left: the observed (solid) and expected (dashed) 95% CL upper limits on the signal strength of HH production from the combination of Run 2 and Run 3 analyses and for each data set separately. The orange and blue bands represent, respectively, the 68 and 95% CL intervals around the expected limit. Right: the same breakdown of 95% CL upper limits on the signal strength of $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ production. |
png pdf |
Figure 29-b:
Left: the observed (solid) and expected (dashed) 95% CL upper limits on the signal strength of HH production from the combination of Run 2 and Run 3 analyses and for each data set separately. The orange and blue bands represent, respectively, the 68 and 95% CL intervals around the expected limit. Right: the same breakdown of 95% CL upper limits on the signal strength of $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ production. |
png pdf |
Figure 30:
The observed (solid) and expected (dashed) 95% CL upper limits on the signal strength of the HH production ($ \mu_{\mathrm{H}\mathrm{H}} $) obtained as function of $ \kappa_{\lambda} $ (left) and $ \kappa_{\text{2V}} $ (right) for the combined fit of Run 2 and Run 3 $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ analyses. The orange and blue bands represent, respectively, the 68 and 95% CL intervals around the expected limit. The horizontal red lines indicate the SM prediction. |
png pdf |
Figure 30-a:
The observed (solid) and expected (dashed) 95% CL upper limits on the signal strength of the HH production ($ \mu_{\mathrm{H}\mathrm{H}} $) obtained as function of $ \kappa_{\lambda} $ (left) and $ \kappa_{\text{2V}} $ (right) for the combined fit of Run 2 and Run 3 $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ analyses. The orange and blue bands represent, respectively, the 68 and 95% CL intervals around the expected limit. The horizontal red lines indicate the SM prediction. |
png pdf |
Figure 30-b:
The observed (solid) and expected (dashed) 95% CL upper limits on the signal strength of the HH production ($ \mu_{\mathrm{H}\mathrm{H}} $) obtained as function of $ \kappa_{\lambda} $ (left) and $ \kappa_{\text{2V}} $ (right) for the combined fit of Run 2 and Run 3 $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ analyses. The orange and blue bands represent, respectively, the 68 and 95% CL intervals around the expected limit. The horizontal red lines indicate the SM prediction. |
png pdf |
Figure 31:
The observed (solid) and expected (dashed) profile likelihood ratios as function of $ \kappa_{\lambda} $ (left) and $ \kappa_{\text{2V}} $ (right) for the combined fit of Run 2 and Run 3 $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ analyses, where the expected is obtained from an Asimov dataset [111] defined by fixing the nuisance parameters to values obtained from a fit to data with $ {\mu_{\mathrm{H}\mathrm{H}}=1} $. The $ {q_{\kappa} < 1} $ and $ {q_{\kappa} < 3.84} $ levels are indicated with the dashed red lines. |
png pdf |
Figure 31-a:
The observed (solid) and expected (dashed) profile likelihood ratios as function of $ \kappa_{\lambda} $ (left) and $ \kappa_{\text{2V}} $ (right) for the combined fit of Run 2 and Run 3 $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ analyses, where the expected is obtained from an Asimov dataset [111] defined by fixing the nuisance parameters to values obtained from a fit to data with $ {\mu_{\mathrm{H}\mathrm{H}}=1} $. The $ {q_{\kappa} < 1} $ and $ {q_{\kappa} < 3.84} $ levels are indicated with the dashed red lines. |
png pdf |
Figure 31-b:
The observed (solid) and expected (dashed) profile likelihood ratios as function of $ \kappa_{\lambda} $ (left) and $ \kappa_{\text{2V}} $ (right) for the combined fit of Run 2 and Run 3 $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ analyses, where the expected is obtained from an Asimov dataset [111] defined by fixing the nuisance parameters to values obtained from a fit to data with $ {\mu_{\mathrm{H}\mathrm{H}}=1} $. The $ {q_{\kappa} < 1} $ and $ {q_{\kappa} < 3.84} $ levels are indicated with the dashed red lines. |
png pdf |
Figure 32:
The observed and expected 2D exclusion range for ($ \mu_{\mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H}} $, $ \mu_{\mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H}} $) (left) and ($ \kappa_{\lambda} $,$ \kappa_{\text{2V}} $) (right) for the combination of Run 2 and Run 3 $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ analyses. The black solid and dashed lines show the observed $ {q_{\kappa} < 2.30} $ and $ {q_{\kappa} < 5.99} $ exclusion contours, respectively, while the orange and blue shaded regions show the expected exclusion contours. The red circle indicates the SM prediction, while the black cross shows the best fit result. The likelihood function is not well defined in regions with large negative values of $ \mu_{\mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H}} $, where the total prediction for the sum of signal and backgrounds can become negative in some categories of the input analyses. This region is therefore not shown. |
png pdf |
Figure 32-a:
The observed and expected 2D exclusion range for ($ \mu_{\mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H}} $, $ \mu_{\mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H}} $) (left) and ($ \kappa_{\lambda} $,$ \kappa_{\text{2V}} $) (right) for the combination of Run 2 and Run 3 $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ analyses. The black solid and dashed lines show the observed $ {q_{\kappa} < 2.30} $ and $ {q_{\kappa} < 5.99} $ exclusion contours, respectively, while the orange and blue shaded regions show the expected exclusion contours. The red circle indicates the SM prediction, while the black cross shows the best fit result. The likelihood function is not well defined in regions with large negative values of $ \mu_{\mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H}} $, where the total prediction for the sum of signal and backgrounds can become negative in some categories of the input analyses. This region is therefore not shown. |
png pdf |
Figure 32-b:
The observed and expected 2D exclusion range for ($ \mu_{\mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H}} $, $ \mu_{\mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H}} $) (left) and ($ \kappa_{\lambda} $,$ \kappa_{\text{2V}} $) (right) for the combination of Run 2 and Run 3 $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ analyses. The black solid and dashed lines show the observed $ {q_{\kappa} < 2.30} $ and $ {q_{\kappa} < 5.99} $ exclusion contours, respectively, while the orange and blue shaded regions show the expected exclusion contours. The red circle indicates the SM prediction, while the black cross shows the best fit result. The likelihood function is not well defined in regions with large negative values of $ \mu_{\mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H}} $, where the total prediction for the sum of signal and backgrounds can become negative in some categories of the input analyses. This region is therefore not shown. |
| Tables | |
png pdf |
Table 1:
The observed and expected upper limits at 95% CL on the $ \mu_{\mathrm{Z}\mathrm{Z}} $ and $ \mu_{\mathrm{Z}\mathrm{H}} $, observed signal strength and significance for the Run 2 [32] and Run 3 data sets and their combination. The upper limits on $ \mu_{\mathrm{Z}\mathrm{Z}} $ ($ \mu_{\mathrm{Z}\mathrm{H}} $) are obtained from a fit on the $\mathcal{D}(\mathrm{ZZ-vs-bkg})$ ($\mathcal{D}(\mathrm{ZH-vs-bkg})$) score under the hypothesis of no ZZ (ZH) signal, while the $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $, $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ and ZH (ZZ) processes are fixed to their SM values. The uncertainties given correspond to the 68% CL intervals. |
png pdf |
Table 2:
Expected best fit values and 95% CL upper limits (U.L.) for $ \mu_{\mathrm{H}\mathrm{H}} $, $ \mu_{\mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H}} $, and $ \mu_{\mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H}} $ in the $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ resolved analysis following the two approaches described in Sections 5.2 and 5.3, respectively. The uncertainties given for the best fit signal strengths correspond to the 68% CL intervals. The best fit signal strengths are calculated with a SM $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ signal injected, while the upper limits are calculated in the absence of signal. |
png pdf |
Table 3:
Observed best fit values and 95% CL upper limits (U.L.) for $ \mu_{\mathrm{H}\mathrm{H}} $, $ \mu_{\mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H}} $, and $ \mu_{\mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H}} $ in the $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ resolved analysis following the two approaches described in Sections 5.2 and 5.3, respectively. The uncertainties given for the best fit signal strengths correspond to the 68% CL intervals. |
png pdf |
Table 4:
Observed and expected, in absence of signal, intervals for $ \kappa_{\lambda} $ and $ \kappa_{\text{2V}} $ outside of which the theoretical prediction for the HH cross section is excluded at 95% CL in the $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ resolved analysis, following the two approaches described in Sections 5.2 and 5.3, respectively. |
png pdf |
Table 5:
Expected and observed $ \mu_{\mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H}} $ and their corresponding upper limits (U.L.) at 95% CL in the $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ resolved channel with Run 2 data (described in Section 5.4). The observed and expected intervals outside of which the theoretical prediction for the HH cross section is excluded at 95% CL for $ \kappa_{\lambda} $ are also reported. |
png pdf |
Table 6:
The expected best fit signal strengths and 95% CL upper limits (U.L.) on the inclusive, $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $, and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ signal strengths for the SM scenario for the two approaches described in Sections 6.2 and 6.3, respectively. The expected best fit signal strength and 95% CL upper limit on the $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ signal strength for the non-SM $ \kappa_{\text{2V}}= $ 0.5 scenario are also reported. The uncertainties given for the best fit signal strengths correspond to the 68% CL intervals. The best fit signal strengths are calculated with a SM $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ signal injected, while the upper limits are calculated in the absence of signal. |
png pdf |
Table 7:
The observed best fit signal strengths and 95% CL upper limits (U.L.) on the inclusive, $ \mathrm{g}\mathrm{g}\mathrm{H}\mathrm{H} $, and $ \mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H} $ signal strengths for the SM scenario for the two approaches described in Sections 6.2 and 6.3, respectively. The best fit and observed 95% CL upper limits on the HH signal strength for the non-SM $ \kappa_{\text{2V}}= $ 0.5 are also reported. The uncertainties given for the best fit signal strengths correspond to the 68% CL intervals. |
png pdf |
Table 8:
The observed and expected intervals for which the predicted HH production cross section is excluded at 95% CL, for $ \kappa_{\lambda} $ and $ \kappa_{\text{2V}} $, in the merged analysis for the two approaches described in Sections 6.2 and 6.3, respectively. |
png pdf |
Table 9:
Expected and observed best fit values for the signal strengths, $ \kappa_{\lambda} $, and $ \kappa_{\text{2V}} $ from the combined fit of the $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ resolved and merged analyses. The uncertainties given correspond to the $ {q_{\kappa} < 1} $ intervals. The expected results are calculated with a SM $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ signal injected. |
png pdf |
Table 10:
Major sources of uncertainty in the measurement of the $ \mu_{\mathrm{H}\mathrm{H}} $ signal strength for the combined fit of the resolved and merged $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ analyses. The post-fit uncertainty on $ \mu_{\mathrm{H}\mathrm{H}} $ is separated into overall statistical and systematic components. The systematic component is further divided into uncertainties affecting the background prediction and those affecting the HH signal prediction. |
png pdf |
Table 11:
Expected and observed best fit values for the signal strengths, $ \kappa_{\lambda} $, and $ \kappa_{\text{2V}} $ from the combined fit of the $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ Run 2 and Run 3 analyses. The uncertainties given correspond to the $ {q_{\kappa} < 1} $ intervals. The expected results are calculated with a SM $ \mathrm{H}\mathrm{H}\to4\mathrm{b} $ signal injected. No lower bound is provided for the observed $ \mu_{\mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H}} $ because the likelihood function is not well defined in regions with large negative values of $ \mu_{\mathrm{q}\mathrm{q}\mathrm{H}\mathrm{H}} $. |
| Summary |
| Measurements of Higgs boson pair (HH) production in the four bottom quark (4b) final state are presented using proton-proton (pp) collision data at $ \sqrt{s}= $ 13.6 TeV collected by the CMS experiment at the CERN LHC during 2022--2023 and corresponding to an integrated luminosity of 62 fb$ ^{-1} $. Events in which the Higgs boson decays, $ \mathrm{H} \to \mathrm{b}\overline{\mathrm{b}} $, are separately reconstructed as pairs of small-radius jets (resolved), as well as those where they are reconstructed as single large-radius jets (merged), are studied exclusively. Benefiting from new methods in trigger selection, event selection, and signal extraction, the combination of analyses in the resolved and merged topologies gives an observed (expected) upper limit on the HH signal strength, $ \mu_{\mathrm{H}\mathrm{H}} $, of 4.4 (4.4) at 95% confidence level (CL). Compared to previously published LHC results in the 4b final state, the expected limit with an equivalent integrated luminosity is improved by more than a factor of two in the resolved topology and is better in the merged topology as well. An updated analysis of the resolved topology using 138 fb$ ^{-1} $ of 13 TeV pp collision data yields an observed (expected) 95% CL upper limit on $ \mu_{\mathrm{H}\mathrm{H}} $ of 10.0 (5.9), an improvement of about 25% in the expected limit compared to the published results using the same data. Results in the 4b final state with 13 and 13.6 TeV are combined, resulting in an observed (expected) 95% CL upper limit on $ \mu_{\mathrm{H}\mathrm{H}} $ of 4.7 (2.8). The allowed ranges for the Higgs boson trilinear self-coupling and quartic coupling between two Higgs bosons and two vector bosons, relative to the standard model expectation, in which the test statistic $ {q_{\kappa} = -2\Delta \ln \mathcal{L}} $ is less than 3.84 are observed (expected) to be $ {[-4.1, 9.6]} {([-3.2, 9.8])} $ and $ {[0.77, 1.27]} {([0.69, 1.35])} $, respectively. These are the most stringent constraints achieved in the 4b final state to date. |
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