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| CMS-PAS-B2G-24-007 | ||
| Search for heavy H$ \gamma $ and Z$ \gamma $ resonances with a b quark pair in the final state in proton-proton collisions at $ \sqrt{s} = $ 13 TeV | ||
| CMS Collaboration | ||
| 21 May 2025 | ||
| Abstract: A search for heavy resonances decaying into a H or Z boson and a photon, with the H and Z bosons decaying to a pair of bottom quarks ($\mathrm{b\bar{b}}$) is presented. The analysis is performed in proton-proton collision data at $ \sqrt{s}= $ 13 TeV collected by the CMS experiment at the CERN LHC, corresponding to an integrated luminosity of 138 fb$ ^{-1} $. The analyzed events include a photon and a massive, large-radius jet with a significant Lorentz boost containing the $\mathrm{b\bar{b}}$ system and is identified as a candidate for the H or Z boson. An advanced flavor tagging algorithm based on the transformer architecture is used to classify jets into 314 categories based on their substructures, and it is employed to efficiently identify and select H and Z boson candidate jets decaying into $\mathrm{b\bar{b}}$, suppressing background. A set of parametric functions is used to fit the photon-jet invariant mass spectrum and to extract potential signals. No significant excess above standard model expectations is observed. The results are interpreted as upper limits on the product of the production cross section and branching fraction for narrow spin-1 H$ \gamma $ resonances and for spin-0 Z$ \gamma $ resonances of various widths. These limits are the most stringent to date. | ||
| Links: CDS record (PDF) ; CADI line (restricted) ; | ||
| Figures | |
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Figure 1:
Schematic illustration indicating the topology of the signal processes under consideration in this search. The spin-1 $ \mathrm{Z}' \to\mathrm{H}\gamma\to\mathrm{b}\overline{\mathrm{b}}\gamma $ scenario is shown to the left, and the spin-0 $ \mathrm{S}\to\mathrm{Z}\gamma\to\mathrm{b}\overline{\mathrm{b}}\gamma $ signal scenario is shown on the right. |
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Figure 1-a:
Schematic illustration indicating the topology of the signal processes under consideration in this search. The spin-1 $ \mathrm{Z}' \to\mathrm{H}\gamma\to\mathrm{b}\overline{\mathrm{b}}\gamma $ scenario is shown to the left, and the spin-0 $ \mathrm{S}\to\mathrm{Z}\gamma\to\mathrm{b}\overline{\mathrm{b}}\gamma $ signal scenario is shown on the right. |
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Figure 1-b:
Schematic illustration indicating the topology of the signal processes under consideration in this search. The spin-1 $ \mathrm{Z}' \to\mathrm{H}\gamma\to\mathrm{b}\overline{\mathrm{b}}\gamma $ scenario is shown to the left, and the spin-0 $ \mathrm{S}\to\mathrm{Z}\gamma\to\mathrm{b}\overline{\mathrm{b}}\gamma $ signal scenario is shown on the right. |
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Figure 2:
Shown on the left is a plot of background efficiency ($ \epsilon_\text{B} $) vs.\ signal efficiency ($ \epsilon_\text{S} $), commonly referred to as a ROC curve (as defined in the text), for the Xbb tagger, alongside those of various other tagging algorithms used in earlier related analyses. The taggers in the legend are listed in chronological order, from top to bottom, based on their first use in similar searches, which can be found in Refs. [10,68,69,70]. All ROCs are evaluated in the simulated $ m_{ \mathrm{Z}' } = $ 1 TeV signal sample and simulated background samples. The right plot presents the distributions of the Xbb tagger score in data and simulated signal samples. The lower panel illustrates a figure of merit for signal sensitivity, defined as the signal event yield (S) divided by the square root of the data event yield (D), $ S/\sqrt{D} $. Both plots are made using the preselection criteria. The Z' signals are normalized with the benchmark cross sections, while the S signals are normalized to 10 fb. |
png pdf |
Figure 2-a:
Shown on the left is a plot of background efficiency ($ \epsilon_\text{B} $) vs.\ signal efficiency ($ \epsilon_\text{S} $), commonly referred to as a ROC curve (as defined in the text), for the Xbb tagger, alongside those of various other tagging algorithms used in earlier related analyses. The taggers in the legend are listed in chronological order, from top to bottom, based on their first use in similar searches, which can be found in Refs. [10,68,69,70]. All ROCs are evaluated in the simulated $ m_{ \mathrm{Z}' } = $ 1 TeV signal sample and simulated background samples. The right plot presents the distributions of the Xbb tagger score in data and simulated signal samples. The lower panel illustrates a figure of merit for signal sensitivity, defined as the signal event yield (S) divided by the square root of the data event yield (D), $ S/\sqrt{D} $. Both plots are made using the preselection criteria. The Z' signals are normalized with the benchmark cross sections, while the S signals are normalized to 10 fb. |
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Figure 2-b:
Shown on the left is a plot of background efficiency ($ \epsilon_\text{B} $) vs.\ signal efficiency ($ \epsilon_\text{S} $), commonly referred to as a ROC curve (as defined in the text), for the Xbb tagger, alongside those of various other tagging algorithms used in earlier related analyses. The taggers in the legend are listed in chronological order, from top to bottom, based on their first use in similar searches, which can be found in Refs. [10,68,69,70]. All ROCs are evaluated in the simulated $ m_{ \mathrm{Z}' } = $ 1 TeV signal sample and simulated background samples. The right plot presents the distributions of the Xbb tagger score in data and simulated signal samples. The lower panel illustrates a figure of merit for signal sensitivity, defined as the signal event yield (S) divided by the square root of the data event yield (D), $ S/\sqrt{D} $. Both plots are made using the preselection criteria. The Z' signals are normalized with the benchmark cross sections, while the S signals are normalized to 10 fb. |
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Figure 3:
The left and right plots present the distributions of $ m_{\text{j}} $ and $ m_{\text{j}\gamma} $, respectively, in data, MC background, and simulated signal samples, with preselection criteria applied. The Z' signals are normalized using the benchmark cross sections, while the S signals are normalized to 10 fb. |
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Figure 3-a:
The left and right plots present the distributions of $ m_{\text{j}} $ and $ m_{\text{j}\gamma} $, respectively, in data, MC background, and simulated signal samples, with preselection criteria applied. The Z' signals are normalized using the benchmark cross sections, while the S signals are normalized to 10 fb. |
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Figure 3-b:
The left and right plots present the distributions of $ m_{\text{j}} $ and $ m_{\text{j}\gamma} $, respectively, in data, MC background, and simulated signal samples, with preselection criteria applied. The Z' signals are normalized using the benchmark cross sections, while the S signals are normalized to 10 fb. |
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Figure 4:
A schematic illustration of the definition of the SRs and CRs in the $ m_{\text{j}} $–Xbb plane is presented to the left. The right is a plot of the product of signal efficiency and acceptance vs.\ resonance mass for the simulated signal samples in the relevant SRs. |
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Figure 4-a:
A schematic illustration of the definition of the SRs and CRs in the $ m_{\text{j}} $–Xbb plane is presented to the left. The right is a plot of the product of signal efficiency and acceptance vs.\ resonance mass for the simulated signal samples in the relevant SRs. |
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Figure 4-b:
A schematic illustration of the definition of the SRs and CRs in the $ m_{\text{j}} $–Xbb plane is presented to the left. The right is a plot of the product of signal efficiency and acceptance vs.\ resonance mass for the simulated signal samples in the relevant SRs. |
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Figure 5:
The $ m_{\text{j}\gamma} $ distributions of the data in CR1 (left) and CR2 (right) fitted with the six parametric functions. The lower panels show the pull distributions as defined in the text, with respect to the best fit function. |
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Figure 5-a:
The $ m_{\text{j}\gamma} $ distributions of the data in CR1 (left) and CR2 (right) fitted with the six parametric functions. The lower panels show the pull distributions as defined in the text, with respect to the best fit function. |
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Figure 5-b:
The $ m_{\text{j}\gamma} $ distributions of the data in CR1 (left) and CR2 (right) fitted with the six parametric functions. The lower panels show the pull distributions as defined in the text, with respect to the best fit function. |
png pdf |
Figure 6:
Postfit $ m_{\text{j}\gamma} $ spectra in the four SRs: SRH1 (upper left), SRZ1 (upper right), SRH2 (lower left), and SRZ2 (lower right). The lower panels show the pull distributions with respect to the best-fit function. The signals with the largest local significances are shown in each SR and are normalized to the observed cross section upper limits. |
png pdf |
Figure 6-a:
Postfit $ m_{\text{j}\gamma} $ spectra in the four SRs: SRH1 (upper left), SRZ1 (upper right), SRH2 (lower left), and SRZ2 (lower right). The lower panels show the pull distributions with respect to the best-fit function. The signals with the largest local significances are shown in each SR and are normalized to the observed cross section upper limits. |
png pdf |
Figure 6-b:
Postfit $ m_{\text{j}\gamma} $ spectra in the four SRs: SRH1 (upper left), SRZ1 (upper right), SRH2 (lower left), and SRZ2 (lower right). The lower panels show the pull distributions with respect to the best-fit function. The signals with the largest local significances are shown in each SR and are normalized to the observed cross section upper limits. |
png pdf |
Figure 6-c:
Postfit $ m_{\text{j}\gamma} $ spectra in the four SRs: SRH1 (upper left), SRZ1 (upper right), SRH2 (lower left), and SRZ2 (lower right). The lower panels show the pull distributions with respect to the best-fit function. The signals with the largest local significances are shown in each SR and are normalized to the observed cross section upper limits. |
png pdf |
Figure 6-d:
Postfit $ m_{\text{j}\gamma} $ spectra in the four SRs: SRH1 (upper left), SRZ1 (upper right), SRH2 (lower left), and SRZ2 (lower right). The lower panels show the pull distributions with respect to the best-fit function. The signals with the largest local significances are shown in each SR and are normalized to the observed cross section upper limits. |
png pdf |
Figure 7:
The 95% CL upper limits on the product of production cross section and branching fraction $ \sigma\mathcal{B} $ for $ \mathrm{Z}' \to\mathrm{H}\gamma $ (upper left) and $ \mathrm{S}\to\mathrm{Z}\gamma $ with narrow width (upper right), 5.6% width (lower left), and 10% width (lower right). Observed (expected) limits are shown with solid (dashed) lines. The colored bands represent the 68% and 95% CL intervals for the expected limits. The red line represents the theory benchmark model used for Z' signal simulation. |
png pdf |
Figure 7-a:
The 95% CL upper limits on the product of production cross section and branching fraction $ \sigma\mathcal{B} $ for $ \mathrm{Z}' \to\mathrm{H}\gamma $ (upper left) and $ \mathrm{S}\to\mathrm{Z}\gamma $ with narrow width (upper right), 5.6% width (lower left), and 10% width (lower right). Observed (expected) limits are shown with solid (dashed) lines. The colored bands represent the 68% and 95% CL intervals for the expected limits. The red line represents the theory benchmark model used for Z' signal simulation. |
png pdf |
Figure 7-b:
The 95% CL upper limits on the product of production cross section and branching fraction $ \sigma\mathcal{B} $ for $ \mathrm{Z}' \to\mathrm{H}\gamma $ (upper left) and $ \mathrm{S}\to\mathrm{Z}\gamma $ with narrow width (upper right), 5.6% width (lower left), and 10% width (lower right). Observed (expected) limits are shown with solid (dashed) lines. The colored bands represent the 68% and 95% CL intervals for the expected limits. The red line represents the theory benchmark model used for Z' signal simulation. |
png pdf |
Figure 7-c:
The 95% CL upper limits on the product of production cross section and branching fraction $ \sigma\mathcal{B} $ for $ \mathrm{Z}' \to\mathrm{H}\gamma $ (upper left) and $ \mathrm{S}\to\mathrm{Z}\gamma $ with narrow width (upper right), 5.6% width (lower left), and 10% width (lower right). Observed (expected) limits are shown with solid (dashed) lines. The colored bands represent the 68% and 95% CL intervals for the expected limits. The red line represents the theory benchmark model used for Z' signal simulation. |
png pdf |
Figure 7-d:
The 95% CL upper limits on the product of production cross section and branching fraction $ \sigma\mathcal{B} $ for $ \mathrm{Z}' \to\mathrm{H}\gamma $ (upper left) and $ \mathrm{S}\to\mathrm{Z}\gamma $ with narrow width (upper right), 5.6% width (lower left), and 10% width (lower right). Observed (expected) limits are shown with solid (dashed) lines. The colored bands represent the 68% and 95% CL intervals for the expected limits. The red line represents the theory benchmark model used for Z' signal simulation. |
| Tables | |
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Table 1:
The sources of systematic uncertainties included in the analysis are detailed. The second column from the left indicates whether an uncertainty applies to the background (B) or signal (S). The next column indicates whether the uncertainty affects the background or signal's shape or its rate. The fourth column from the left lists the magnitude of the corresponding systematic uncertainty. The final column indicates the total number of nuisance parameters (NPs) and whether or not they are treated as correlated across SRs. An asterisk (*) denotes a value or shape template unique to each signal scenario.\\ |
| Summary |
| A search for heavy resonances decaying to a photon and a Z or H boson in the $ \gamma $+jet final state, using 138 fb$ ^{-1} $ of $ \sqrt{s} = $ 13 TeV pp collision data collected by the CMS detector, has been presented. For the $ \mathrm{H}\gamma $ resonance analysis, a benchmark spin-1 resonance model with a small fractional width is considered, while the $ \mathrm{Z}\gamma $ analysis considers a SM Higgs-like heavy spin-0 resonance, using several different width hypotheses. The final states of these resonant processes feature a photon and a massive, large-radius jet containing the decay products of $ \mathrm{H},\mathrm{Z}\to\mathrm{b}\overline{\mathrm{b}} $, identified using the PARTICLE TRANSFORMER jet substructure algorithm for jet classification and the PARTICLENET algorithm for jet mass regression. These advanced machine learning techniques greatly improve sensitivity over previous searches. The results are found to be consistent with the standard model predictions within measurement uncertainties. Exclusion limits are set at 95% confidence level as a function of the resonance mass on the product of the production cross section and branching fraction to a photon and a Z or H boson. This result establishes the most stringent constraints to date on the production of such heavy resonances. |
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Compact Muon Solenoid LHC, CERN |
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