CMS-PAS-HIG-24-015

CMS-PAS-HIG-24-015
Search for triple Higgs production in Run 2 data of CMS using 4 $ b 2\gamma $ final state
CMS Collaboration
2025-07-07

Abstract: A search for triple Higgs boson production using the Run 2 data collected by the CMS experiment in the final state of four b jets and two photons is presented. The search is performed with proton-proton collision data at $ \sqrt{s}= $ 13 TeV, corresponding to an integrated luminosity of 138 fb$ ^{-1} $. The observed (expected) upper limit on the inclusive HHH production cross section is found to be 244 (152) fb at 95% confidence level. The results are interpreted in the $ \kappa $-framework and are parameterized as limits on the scaling coefficients of the trilinear ($ \lambda_3 $) and quartic ($ \lambda_4 $) Higgs boson self-coupling.
Links: CDS record (PDF) ; CADI line (restricted) ;

Figures	Summary	References	CMS Publications

Figures
png pdf	Figure 1: Representative Feynman diagrams for HHH production via gluon-gluon fusion mode at LO. The vertices having contributions for $ \lambda_3 $ and $ \lambda_4 $ are marked in orange and blue, respectively.
png pdf	Figure 1-a: Representative Feynman diagrams for HHH production via gluon-gluon fusion mode at LO. The vertices having contributions for $ \lambda_3 $ and $ \lambda_4 $ are marked in orange and blue, respectively.
png pdf	Figure 1-b: Representative Feynman diagrams for HHH production via gluon-gluon fusion mode at LO. The vertices having contributions for $ \lambda_3 $ and $ \lambda_4 $ are marked in orange and blue, respectively.
png pdf	Figure 1-c: Representative Feynman diagrams for HHH production via gluon-gluon fusion mode at LO. The vertices having contributions for $ \lambda_3 $ and $ \lambda_4 $ are marked in orange and blue, respectively.
png pdf	Figure 1-d: Representative Feynman diagrams for HHH production via gluon-gluon fusion mode at LO. The vertices having contributions for $ \lambda_3 $ and $ \lambda_4 $ are marked in orange and blue, respectively.
png pdf	Figure 2: Distributions of the reconstructed masses of the leading (H1) and subleading (H2) $ \mathrm{H}\to\mathrm{b}\overline{\mathrm{b}} $ candidates for signal events (left) and non-resonant backgrounds (right).
png pdf	Figure 2-a: Distributions of the reconstructed masses of the leading (H1) and subleading (H2) $ \mathrm{H}\to\mathrm{b}\overline{\mathrm{b}} $ candidates for signal events (left) and non-resonant backgrounds (right).
png pdf	Figure 2-b: Distributions of the reconstructed masses of the leading (H1) and subleading (H2) $ \mathrm{H}\to\mathrm{b}\overline{\mathrm{b}} $ candidates for signal events (left) and non-resonant backgrounds (right).
png pdf	Figure 3: Distribution of the reconstructed diphoton invariant mass in data (black points) and the predicted backgrounds (colored histograms) after event morphing. The vertical bars on the data points represent the statistical uncertainties associated with the data. The lower panel shows the ratio of data to the sum of background predictions. The light blue band in the lower panel represents the statistical uncertainty in the background predictions.
png pdf	Figure 4: Distribution of the BDT$ _{\text{nonres}} $ (left) and BDT$ _{\text{res}} $ (right) scores in data (black points) and predicted signal and backgrounds (colored histograms) after event morphing. The vertical error bars on the data points represent the statistical uncertainties in data. The violet line represents the expected distribution of signal HHH events, scaled by a factor of 10$ ^6 $. The lower panel shows the ratio of data to the sum of background predictions. The light blue band in the lower panel represents the statistical uncertainty in the background predictions. The imperfect description of the data by the simulation is not a concern in this context, as the scores from simulation are used solely for optimizing selections. The final background model for the non-resonant component is derived directly from the data. A highly magnified distribution due to the HH signal has been superposed for illustration.
png pdf	Figure 4-a: Distribution of the BDT$ _{\text{nonres}} $ (left) and BDT$ _{\text{res}} $ (right) scores in data (black points) and predicted signal and backgrounds (colored histograms) after event morphing. The vertical error bars on the data points represent the statistical uncertainties in data. The violet line represents the expected distribution of signal HHH events, scaled by a factor of 10$ ^6 $. The lower panel shows the ratio of data to the sum of background predictions. The light blue band in the lower panel represents the statistical uncertainty in the background predictions. The imperfect description of the data by the simulation is not a concern in this context, as the scores from simulation are used solely for optimizing selections. The final background model for the non-resonant component is derived directly from the data. A highly magnified distribution due to the HH signal has been superposed for illustration.
png pdf	Figure 4-b: Distribution of the BDT$ _{\text{nonres}} $ (left) and BDT$ _{\text{res}} $ (right) scores in data (black points) and predicted signal and backgrounds (colored histograms) after event morphing. The vertical error bars on the data points represent the statistical uncertainties in data. The violet line represents the expected distribution of signal HHH events, scaled by a factor of 10$ ^6 $. The lower panel shows the ratio of data to the sum of background predictions. The light blue band in the lower panel represents the statistical uncertainty in the background predictions. The imperfect description of the data by the simulation is not a concern in this context, as the scores from simulation are used solely for optimizing selections. The final background model for the non-resonant component is derived directly from the data. A highly magnified distribution due to the HH signal has been superposed for illustration.
png pdf	Figure 5: Schematic representation of the analysis category definition in the two-dimensional plane of BDT$ _{\text{nonres}} $ and BDT$ _{\text{res}} $.
png pdf	Figure 6: Left: Parameterized signal shape for $ m_{\gamma\gamma} $. The open squares represent the simulated events and the blue lines are corresponding models. The corresponding interval as a gray band shows the $ \sigma_{\text{eff}} $ value (half the width of the narrowest interval containing 68.3% of the invariant mass distribution). Right: Invariant mass distribution of $ m_{\gamma\gamma} $ for the selected events in data (black points) from all analysis categories. The solid red lines demonstrates the fitted signal plus background model and the blue dotted line shows the background component. The lower panel shows the residual post fit signal yield after the background subtraction from data.
png pdf	Figure 6-a: Left: Parameterized signal shape for $ m_{\gamma\gamma} $. The open squares represent the simulated events and the blue lines are corresponding models. The corresponding interval as a gray band shows the $ \sigma_{\text{eff}} $ value (half the width of the narrowest interval containing 68.3% of the invariant mass distribution). Right: Invariant mass distribution of $ m_{\gamma\gamma} $ for the selected events in data (black points) from all analysis categories. The solid red lines demonstrates the fitted signal plus background model and the blue dotted line shows the background component. The lower panel shows the residual post fit signal yield after the background subtraction from data.
png pdf	Figure 6-b: Left: Parameterized signal shape for $ m_{\gamma\gamma} $. The open squares represent the simulated events and the blue lines are corresponding models. The corresponding interval as a gray band shows the $ \sigma_{\text{eff}} $ value (half the width of the narrowest interval containing 68.3% of the invariant mass distribution). Right: Invariant mass distribution of $ m_{\gamma\gamma} $ for the selected events in data (black points) from all analysis categories. The solid red lines demonstrates the fitted signal plus background model and the blue dotted line shows the background component. The lower panel shows the residual post fit signal yield after the background subtraction from data.
png pdf	Figure 7: Likelihood scan for the signal strength (left) and inclusive HHH crosssection (right). The blue and orange shows the observed and expected results respectively. The red line on the right plot shows the theoretical HHH crossection of 0.079 fb [11].
png pdf	Figure 7-a: Likelihood scan for the signal strength (left) and inclusive HHH crosssection (right). The blue and orange shows the observed and expected results respectively. The red line on the right plot shows the theoretical HHH crossection of 0.079 fb [11].
png pdf	Figure 7-b: Likelihood scan for the signal strength (left) and inclusive HHH crosssection (right). The blue and orange shows the observed and expected results respectively. The red line on the right plot shows the theoretical HHH crossection of 0.079 fb [11].
png pdf	Figure 8: Likelihood scan, as a function of $ \kappa_{\lambda 3} $ (left) and $ \kappa_{\lambda 4} $ (right). The blue and orange curves show the observed and expected results respectively. The 68% and 95% CL intervals are indicated by the horizontal dashed lines.
png pdf	Figure 8-a: Likelihood scan, as a function of $ \kappa_{\lambda 3} $ (left) and $ \kappa_{\lambda 4} $ (right). The blue and orange curves show the observed and expected results respectively. The 68% and 95% CL intervals are indicated by the horizontal dashed lines.
png pdf	Figure 8-b: Likelihood scan, as a function of $ \kappa_{\lambda 3} $ (left) and $ \kappa_{\lambda 4} $ (right). The blue and orange curves show the observed and expected results respectively. The 68% and 95% CL intervals are indicated by the horizontal dashed lines.
png pdf	Figure 9: Likelihood contours at 95% CL in the ($ \kappa_{\lambda 3} $, $ \kappa_{\lambda 4} $) plane evaluated with an Asimov data set assuming SM hypothesis (in orange line) and the observed data (in blue line). The green shaded region shows the allowed bounds on $ \kappa_{\lambda 3} $ from the H+HH combination measurements [9].

Summary

The first search for triple Higgs boson production (HHH) by the CMS collaboration has been presented, targeting the final state where two of the Higgs bosons decay to a pair of bottom quarks and one to a pair of photons. The search uses proton-proton collision data at $ \sqrt s $ = 13 TeV, corresponding to an integrated luminosity of 138 fb$ ^{-1} $. Upper limits on the product of the HHH production cross section and the $ \mathrm{HHH} \rightarrow4\mathrm{b}2\gamma $ branching fraction are extracted. The observed (expected) upper limit is 0.56 (0.35) fb at 95% confidence level (CL), corresponding to 3400 (2086) times the standard model cross section value. The results are interpreted in the $ \kappa $-framework, setting upper limits on the trilinear and quartic Higgs boson self-coupling modifiers $ \kappa_{\lambda 3} $ = $ \frac{\lambda_3}{\lambda_3^{SM}} $ and $ \kappa_{\lambda 4} $ = $ \frac{\lambda_4}{\lambda_4^{SM}} $ respectively, assuming that the top quark Yukawa coupling is standard model like. The trilinear self-coupling modifier $ \kappa_{\lambda 3} $ is observed (expected) to be constrained within a range of [$-$16.1,20.2] ([$-$13.8,18.0]) at 95% CL. The quartic self-coupling modifier $ \kappa_{\lambda 4} $ is observed (expected) to be constrained within a range of [$-$533,541] ( [$-$397,406] ) at 95% CL. The simultaneous constraints on the $ \kappa_{\lambda 3} $ and $ \kappa_{\lambda 4} $ has also been presented from the two-dimensional likelihood scan.

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