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| CMS-HIG-23-010 ; CERN-EP-2025-010 | ||
| Search for a cH signal in the associated production of at least one charm quark with a Higgs boson in the diphoton decay channel in pp collisions at $ \sqrt{s}= $ 13 TeV | ||
| CMS Collaboration | ||
| 11 March 2025 | ||
| JHEP 11 (2025) 060 | ||
| Abstract: This paper presents the first search for a cH signal sensitive to the coupling of the charm quark (c) to the Higgs boson (H) in the associated production of at least one charm quark with a Higgs boson decaying to two photons. The results are based on a data set of proton-proton collisions at a center-of-mass energy of 13 TeV collected with the CMS experiment at the LHC, corresponding to an integrated luminosity of 138 fb$ ^{-1} $. Assuming the standard model (SM) rates for all other Higgs boson production processes, the observed (expected) upper limit at 95% confidence level on the cH signal strength is 243 (355) times the SM prediction. Under the same assumption, the observed (expected) allowed interval on the Higgs boson to charm quark coupling modifier, $ \kappa_\mathrm{c} $, is $ |\kappa_\mathrm{c}| < $ 38.1 ($ |\kappa_\mathrm{c}| < $ 72.5) at 95% confidence level. | ||
| Links: e-print arXiv:2503.08797 [hep-ex] (PDF) ; CDS record ; inSPIRE record ; CADI line (restricted) ; | ||
| Figures | |
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Figure 1:
Leading order Feynman diagrams that contribute to the $ \mathrm{p}\mathrm{p} \to \mathrm{c}\mathrm{H} $ process. Red dots correspond to vertices where the charm Yukawa coupling modifier $ \kappa_\mathrm{c} $ enters. |
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Figure 1-a:
Leading order Feynman diagrams that contribute to the $ \mathrm{p}\mathrm{p} \to \mathrm{c}\mathrm{H} $ process. Red dots correspond to vertices where the charm Yukawa coupling modifier $ \kappa_\mathrm{c} $ enters. |
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Figure 1-b:
Leading order Feynman diagrams that contribute to the $ \mathrm{p}\mathrm{p} \to \mathrm{c}\mathrm{H} $ process. Red dots correspond to vertices where the charm Yukawa coupling modifier $ \kappa_\mathrm{c} $ enters. |
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Figure 2:
Distributions of the CvsL score for the leading jet in simulated cH signal and resonant background events and sideband data events. Error bars representing the statistical uncertainties in the data are too small to be displayed. Events with CvsL score values below the dashed vertical line are not included in the signal region. |
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Figure 3:
Distributions of BDT1 (left) and BDT2 outputs (right) for simulated cH signal and resonant background events, as well as sideband data (representing the continuous background) events. The BDT1 score discriminates the cH signal from the resonant background. The BDT2 score discriminates the cH signal from the continuous background. The plots are indicative of the shapes of the distributions and should not be interpreted as a comparison of their magnitudes. The error bars correspond to the statistical uncertainties. |
png pdf |
Figure 3-a:
Distributions of BDT1 (left) and BDT2 outputs (right) for simulated cH signal and resonant background events, as well as sideband data (representing the continuous background) events. The BDT1 score discriminates the cH signal from the resonant background. The BDT2 score discriminates the cH signal from the continuous background. The plots are indicative of the shapes of the distributions and should not be interpreted as a comparison of their magnitudes. The error bars correspond to the statistical uncertainties. |
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Figure 3-b:
Distributions of BDT1 (left) and BDT2 outputs (right) for simulated cH signal and resonant background events, as well as sideband data (representing the continuous background) events. The BDT1 score discriminates the cH signal from the resonant background. The BDT2 score discriminates the cH signal from the continuous background. The plots are indicative of the shapes of the distributions and should not be interpreted as a comparison of their magnitudes. The error bars correspond to the statistical uncertainties. |
png pdf |
Figure 4:
Two-dimensional distributions of BDT1 output ($ y $ axis) and BDT2 output (x axis) for cH (upper left), $ \mathrm{g}\mathrm{g}\mathrm{H} $ (upper right), and sideband data (lower) in 2017 event categories. The BDT1 score discriminates the cH signal from the resonant background while the BDT2 score discriminates the cH signal from the continuous background. Event category boundaries on BDT scores are illustrated as dashed lines. |
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Figure 4-a:
Two-dimensional distributions of BDT1 output ($ y $ axis) and BDT2 output (x axis) for cH (upper left), $ \mathrm{g}\mathrm{g}\mathrm{H} $ (upper right), and sideband data (lower) in 2017 event categories. The BDT1 score discriminates the cH signal from the resonant background while the BDT2 score discriminates the cH signal from the continuous background. Event category boundaries on BDT scores are illustrated as dashed lines. |
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Figure 4-b:
Two-dimensional distributions of BDT1 output ($ y $ axis) and BDT2 output (x axis) for cH (upper left), $ \mathrm{g}\mathrm{g}\mathrm{H} $ (upper right), and sideband data (lower) in 2017 event categories. The BDT1 score discriminates the cH signal from the resonant background while the BDT2 score discriminates the cH signal from the continuous background. Event category boundaries on BDT scores are illustrated as dashed lines. |
png pdf |
Figure 4-c:
Two-dimensional distributions of BDT1 output ($ y $ axis) and BDT2 output (x axis) for cH (upper left), $ \mathrm{g}\mathrm{g}\mathrm{H} $ (upper right), and sideband data (lower) in 2017 event categories. The BDT1 score discriminates the cH signal from the resonant background while the BDT2 score discriminates the cH signal from the continuous background. Event category boundaries on BDT scores are illustrated as dashed lines. |
png pdf |
Figure 5:
The shape of the parametric signal model for the analysis category with the tightest requirement in both BDT1 and BDT2 scores (left) and for the sum of all analysis categories (right). The open squares represent simulated events and the blue line represents the corresponding model. Also shown is the $ \sigma_{\text{eff}} $ value (half the width of the narrowest interval containing 68.3% of the diphoton mass distribution) in the grey shaded area. |
png pdf |
Figure 5-a:
The shape of the parametric signal model for the analysis category with the tightest requirement in both BDT1 and BDT2 scores (left) and for the sum of all analysis categories (right). The open squares represent simulated events and the blue line represents the corresponding model. Also shown is the $ \sigma_{\text{eff}} $ value (half the width of the narrowest interval containing 68.3% of the diphoton mass distribution) in the grey shaded area. |
png pdf |
Figure 5-b:
The shape of the parametric signal model for the analysis category with the tightest requirement in both BDT1 and BDT2 scores (left) and for the sum of all analysis categories (right). The open squares represent simulated events and the blue line represents the corresponding model. Also shown is the $ \sigma_{\text{eff}} $ value (half the width of the narrowest interval containing 68.3% of the diphoton mass distribution) in the grey shaded area. |
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Figure 6:
Invariant mass distribution of the selected events in all categories. For each category, the events are scaled by $ \mathrm{S}/(\mathrm{S}+\mathrm{B}) $, where S (B) is the number of expected signal (background) events in the smallest mass window containing 68.3% of the expected signal events. Curves for the signal + background fit (red), separately showing the resonant (black) and continuous (blue) backgrounds, as well as bands representing the 68.3% and 95.5% CL intervals in the background estimation, are overlaid. The middle (lower) panel shows the $ m_{\gamma\gamma} $ distribution after subtracting only the continuous background (subtracting all background components) and overlaying the curve for the fitted signal (purple). |
| Tables | |
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Table 1:
Number of expected signal cH ($ \mathrm{H} \to \gamma\gamma $), resonant background and continuous background events, as well as the resulting signal-over-background ratio (S/B) in the diphoton mass window [122.88, 127.88] GeV for all categories. For each category, the event yields for the three years are summed. The fraction of different production processes contributing to the resonant background ($ \mathrm{g}\mathrm{g}\mathrm{H} $, $ {\mathrm{t}\overline{\mathrm{t}}} \mathrm{H} $, VBF, VH, and $ \mathrm{b}\mathrm{H} $) is also reported. |
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Table 2:
Impacts of several uncertainty groups divided by the total uncertainty in the signal strength measurement. The systematic uncertainties are described in Sec. 8. |
| Summary |
| We have presented the first search for a cH signal sensitive to the coupling of the charm quark (c) to the Higgs boson (H) in the associated production of at least one charm quark with a Higgs boson in proton-proton collisions at a center-of-mass energy of 13 TeV. Assuming the signal strengths of non-cH Higgs production processes in the diphoton decay channel to be as predicted by the standard model (SM), the observed and expected upper limits at 95% confidence level on the cH signal strength are 243 and 355 times the SM predictions, respectively. This search also provides sensitivity to the Yukawa coupling between the Higgs boson and the charm quark. Under the same assumption, the observed (expected) allowed interval on the Higgs boson to charm quark coupling modifier $ \kappa_\mathrm{c} $ is $ |\kappa_\mathrm{c}| < $ 38.1 ($ |\kappa_\mathrm{c}| < $ 72.5) at 95% confidence level. The analysis is presently limited by statistical and theoretical uncertainties. Improvements can be achieved by incorporating complementary Higgs boson decay channels and, as larger data sets become available, implementing more refined selection criteria and categorization strategies, such as discriminating charm and bottom quark jets and constructing classifiers distinguishing between more Higgs boson production modes. |
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