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| CMS-PAS-B2G-18-002 | ||
| Search for heavy resonances in the all-hadronic vector-boson pair final state with a multi-dimensional fit | ||
| CMS Collaboration | ||
| March 2019 | ||
| Abstract: We present a novel fit method in the search for new resonances decaying to WW, WZ, or ZZ boson pairs in the all-hadronic final state using data corresponding to an integrated luminosity of 77.3 fb$^{-1}$ taken with the CMS experiment at the LHC at a centre-of-mass energy of $\sqrt{s}= $ 13 TeV. The search is focussed on resonances with masses above 1.2 TeV, where the decay products of each W or Z boson are expected to be collimated into one single large-radius jet. The signal extraction method is based on a three-dimensional maximum likelihood fit of the dijet invariant mass and the two jet masses, which allows systematic uncertainties that affect all three dimensions to be incorporated simultaneously. The new method yields an improvement in sensitivity of up to 30% with respect to previous search methods used in CMS. No excess is observed above the estimated standard model background. In a heavy vector triplet model, spin-1 W' and Z' resonances with masses below 3.8 and 3.5 TeV, respectively, are excluded at 95% confidence level. In a narrow-width bulk graviton model, upper limits on cross sections are set between 27 and 0.2 fb for resonance masses between 1.2 and 5.2 TeV, respectively. | ||
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Links:
CDS record (PDF) ;
CADI line (restricted) ;
These preliminary results are superseded in this paper, EPJC 80 (2020) 237. The superseded preliminary plots can be found here. |
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| Figures | |
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Figure 1:
The ${\tau _{21}}$ profile dependence on $\rho ' = \log({m_\mathrm {jet}} ^2/ {p_{\mathrm {T}}} /\mu)$ (left). A fit to the linear part of the spectrum yields the slope $M =-0.080$, which is used to define the mass- and ${p_{\mathrm {T}}}$ -decorrelated variable $ {\tau _{21}^\textbf {DDT}} = {\tau _{21}} -M\times \rho '$. The ${\tau _{21}^\textbf {DDT}}$ profile versus $\rho '$ is shown on the right. |
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Figure 1-a:
The ${\tau _{21}}$ profile dependence on $\rho ' = \log({m_\mathrm {jet}} ^2/ {p_{\mathrm {T}}} /\mu)$. A fit to the linear part of the spectrum yields the slope $M =-0.080$, which is used to define the mass- and ${p_{\mathrm {T}}}$ -decorrelated variable $ {\tau _{21}^\textbf {DDT}} = {\tau _{21}} -M\times \rho '$. |
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Figure 1-b:
The ${\tau _{21}^\textbf {DDT}}$ profile versus $\rho '$ is shown. |
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Figure 2:
Left: Performance of ${\tau _{21}}$ and ${\tau _{21}^\textbf {DDT}}$ in the background-signal efficiency plane. Right: Distribution of ${\tau _{21}}$ and ${\tau _{21}^\textbf {DDT}}$ for W-jets and quark or gluon jets from QCD multijet events. |
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Figure 2-a:
Performance of ${\tau _{21}}$ and ${\tau _{21}^\textbf {DDT}}$ in the background-signal efficiency plane. |
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Figure 2-b:
Distribution of ${\tau _{21}}$ and ${\tau _{21}^\textbf {DDT}}$ for W-jets and quark or gluon jets from QCD multijet events. |
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Figure 3:
Jet mass (upper left) and ${\tau _{21}^\textbf {DDT}}$ (upper right) distributions for randomly sorted selected jets, and dijet invariant mass distribution (lower) for events with a jet mass between 55 and 215 GeV in data and simulation. For the QCD multijet simulation, several alternative predictions are shown, scaled to the data minus the other background processes, which are scaled to their SM expectation as described in the text. The different signal distributions are arbitrarily scaled for visibility. No selection on ${\tau _{21}^\textbf {DDT}}$ is applied. |
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Figure 3-a:
Jet mass distribution for randomly sorted selected jets for events with a jet mass between 55 and 215 GeV in data and simulation. For the QCD multijet simulation, several alternative predictions are shown, scaled to the data minus the other background processes, which are scaled to their SM expectation as described in the text. The different signal distributions are arbitrarily scaled for visibility. No selection on ${\tau _{21}^\textbf {DDT}}$ is applied. |
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Figure 3-b:
${\tau _{21}^\textbf {DDT}}$ distribution for randomly sorted selected jets for events with a jet mass between 55 and 215 GeV in data and simulation. For the QCD multijet simulation, several alternative predictions are shown, scaled to the data minus the other background processes, which are scaled to their SM expectation as described in the text. The different signal distributions are arbitrarily scaled for visibility. No selection on ${\tau _{21}^\textbf {DDT}}$ is applied. |
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Figure 3-c:
Dijet invariant mass distribution for events with a jet mass between 55 and 215 GeV in data and simulation. For the QCD multijet simulation, several alternative predictions are shown, scaled to the data minus the other background processes, which are scaled to their SM expectation as described in the text. The different signal distributions are arbitrarily scaled for visibility. No selection on ${\tau _{21}^\textbf {DDT}}$ is applied. |
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Figure 4:
Left: trigger efficiency as a function of the dijet invariant mass using a combination of all analysis triggers. Right: trigger efficiency as a function of the jet mass for triggers requiring an online trimmed mass of at least 30 GeV. The solid yellow markers correspond to the trigger efficiencies for the full 2017 data set and do not reach 100% efficiency due to the jet-mass based triggers being unavailable during a period of data taking (Run B, corresponding to 4.8 fb$^{-1}$). The hollow yellow markers are the corresponding efficiencies excluding this period. Uncertainties shown are statistical only. |
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Figure 4-a:
Trigger efficiency as a function of the dijet invariant mass using a combination of all analysis triggers. The solid yellow markers correspond to the trigger efficiencies for the full 2017 data set and do not reach 100% efficiency due to the jet-mass based triggers being unavailable during a period of data taking (Run B, corresponding to 4.8 fb$^{-1}$). The hollow yellow markers are the corresponding efficiencies excluding this period. Uncertainties shown are statistical only. |
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Figure 4-b:
Trigger efficiency as a function of the jet mass for triggers requiring an online trimmed mass of at least 30 GeV. The solid yellow markers correspond to the trigger efficiencies for the full 2017 data set and do not reach 100% efficiency due to the jet-mass based triggers being unavailable during a period of data taking (Run B, corresponding to 4.8 fb$^{-1}$). The hollow yellow markers are the corresponding efficiencies excluding this period. Uncertainties shown are statistical only. |
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Figure 5:
Jet mass distribution for events that pass (left) and fail (right) the $ {\tau _{21}^\textbf {DDT}} < $ 0.43 selection in the ${{\mathrm {t}\overline {\mathrm {t}}}}$ control sample. The result of the fit to data and simulation is shown by the solid blue and solid red lines, respectively. The background components of the fit are shown as dashed-dotted lines. The fit to 2016 data is shown in the upper panels and the fit to 2017 data in the lower panels. |
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Figure 5-a:
Jet mass distribution for events that pass the $ {\tau _{21}^\textbf {DDT}} < $ 0.43 selection in the ${{\mathrm {t}\overline {\mathrm {t}}}}$ control sample. The result of the fit to data and simulation is shown by the solid blue and solid red lines, respectively. The background components of the fit are shown as dashed-dotted lines. The fit to 2016 data is shown. |
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Figure 5-b:
Jet mass distribution for events that fail the $ {\tau _{21}^\textbf {DDT}} < $ 0.43 selection in the ${{\mathrm {t}\overline {\mathrm {t}}}}$ control sample. The result of the fit to data and simulation is shown by the solid blue and solid red lines, respectively. The background components of the fit are shown as dashed-dotted lines. The fit to 2016 data is shown. |
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Figure 5-c:
Jet mass distribution for events that pass the $ {\tau _{21}^\textbf {DDT}} < $ 0.43 selection in the ${{\mathrm {t}\overline {\mathrm {t}}}}$ control sample. The result of the fit to data and simulation is shown by the solid blue and solid red lines, respectively. The background components of the fit are shown as dashed-dotted lines. The fit to 2017 data is shown. |
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Figure 5-d:
Jet mass distribution for events that fail the $ {\tau _{21}^\textbf {DDT}} < $ 0.43 selection in the ${{\mathrm {t}\overline {\mathrm {t}}}}$ control sample. The result of the fit to data and simulation is shown by the solid blue and solid red lines, respectively. The background components of the fit are shown as dashed-dotted lines. The fit to 2017 data is shown. |
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Figure 6:
Final $ {m_\mathrm {VV}} $ (left) and ${m_\text {jet2}}$ (right) signal shapes extracted from the parametrisation. Shown here is a $ {{\mathrm {G}} _{\text {bulk}}} $ decaying to WW. The ${m_\text {jet2}}$ distribution is for a jet in the HPHP category. |
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Figure 6-a:
Final $ {m_\mathrm {VV}} $ signal shape extracted from the parametrisation. Shown here is a $ {{\mathrm {G}} _{\text {bulk}}} $ decaying to WW. The ${m_\text {jet2}}$ distribution is for a jet in the HPHP category. |
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Figure 6-b:
Final ${m_\text {jet2}}$ signal shape extracted from the parametrisation. Shown here is a $ {{\mathrm {G}} _{\text {bulk}}} $ decaying to WW. The ${m_\text {jet2}}$ distribution is for a jet in the HPHP category. |
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Figure 7:
The mass scale (left) and resolution (right) of the jet as a function of $m_\mathrm {X}$, obtained from the mean and $\sigma $ of the Crystal ball function used to fit the jet mass spectrum. |
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Figure 7-a:
The mass scale of the jet as a function of $m_\mathrm {X}$, obtained from the mean and $\sigma $ of the Crystal ball function used to fit the jet mass spectrum. |
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Figure 7-b:
The resolution of the jet as a function of $m_\mathrm {X}$, obtained from the mean and $\sigma $ of the Crystal ball function used to fit the jet mass spectrum. |
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Figure 8:
Total signal efficiency after all selections are applied as a function of $m_\mathrm {X}$ for signal models with a $ {{\mathrm {G}} _{\text {bulk}}} $ decaying to WW, $ {{\mathrm {G}} _{\text {bulk}}} $ decaying to ZZ, and W' decaying to WZ. The denominator is the number of generated events. The solid and dashed lines show the signal efficiencies for the HPHP and HPLP categories, respectively. |
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Figure 9:
Nominal QCD multijet simulation using PYTHIA 8 (markers) and derived kernel using a forward-folding kernel approach (black solid line), shown together with the five alternate shapes that are added to the fit as shape nuisance parameters. The shapes for the high purity category (left) and low purity category (right) obtained with the 2017 simulation are shown. |
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Figure 9-a:
Nominal QCD multijet simulation using PYTHIA 8 (markers) and derived kernel using a forward-folding kernel approach (black solid line), shown together with the five alternate shapes that are added to the fit as shape nuisance parameters. The shape for the high purity category obtained with the 2017 simulation is shown. |
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Figure 9-b:
Nominal QCD multijet simulation using PYTHIA 8 (markers) and derived kernel using a forward-folding kernel approach (black solid line), shown together with the five alternate shapes that are added to the fit as shape nuisance parameters. The shape for the low purity category obtained with the 2017 simulation is shown. |
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Figure 9-c:
Nominal QCD multijet simulation using PYTHIA 8 (markers) and derived kernel using a forward-folding kernel approach (black solid line), shown together with the five alternate shapes that are added to the fit as shape nuisance parameters. The shape for the high purity category obtained with the 2017 simulation is shown. |
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Figure 9-d:
Nominal QCD multijet simulation using PYTHIA 8 (markers) and derived kernel using a forward-folding kernel approach (black solid line), shown together with the five alternate shapes that are added to the fit as shape nuisance parameters. The shape for the low purity category obtained with the 2017 simulation is shown. |
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Figure 9-e:
Nominal QCD multijet simulation using PYTHIA 8 (markers) and derived kernel using a forward-folding kernel approach (black solid line), shown together with the five alternate shapes that are added to the fit as shape nuisance parameters. The shape for the high purity category obtained with the 2017 simulation is shown. |
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Figure 9-f:
Nominal QCD multijet simulation using PYTHIA 8 (markers) and derived kernel using a forward-folding kernel approach (black solid line), shown together with the five alternate shapes that are added to the fit as shape nuisance parameters. The shape for the low purity category obtained with the 2017 simulation is shown. |
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Figure 10:
Comparison between the fitted result and data distributions of ${m_\text {jet1}}$ (left), ${m_\text {jet2}}$ (middle), and ${m_\mathrm {VV}}$ (right) in the HPHP category. The background shape uncertainty is shown as a red shaded band, and the statistical uncertainties of the data are shown as vertical bars. An example of a signal distribution is overlaid, using an arbitrary normalisation. The corresponding pull distributions (Data-fit)/$\sigma $, where $\sigma = \sqrt {\sigma _\mathrm {data}^2 - \sigma _\mathrm {fit}^2}$ for a bin in ${m_\mathrm {VV}}$ to ensure a Gaussian pull-distribution as defined in [78], are shown below each ${m_\mathrm {VV}}$ plot. |
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Figure 10-a:
Comparison between the fitted result and data distributions of ${m_\text {jet1}}$ in the HPHP category. The background shape uncertainty is shown as a red shaded band, and the statistical uncertainties of the data are shown as vertical bars. An example of a signal distribution is overlaid, using an arbitrary normalisation. The corresponding pull distributions (Data-fit)/$\sigma $, where $\sigma = \sqrt {\sigma _\mathrm {data}^2 - \sigma _\mathrm {fit}^2}$ for a bin in ${m_\mathrm {VV}}$ to ensure a Gaussian pull-distribution as defined in [78], are shown below the ${m_\mathrm {VV}}$ plot. |
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Figure 10-b:
Comparison between the fitted result and data distributions of ${m_\text {jet2}}$ in the HPHP category. The background shape uncertainty is shown as a red shaded band, and the statistical uncertainties of the data are shown as vertical bars. An example of a signal distribution is overlaid, using an arbitrary normalisation. The corresponding pull distributions (Data-fit)/$\sigma $, where $\sigma = \sqrt {\sigma _\mathrm {data}^2 - \sigma _\mathrm {fit}^2}$ for a bin in ${m_\mathrm {VV}}$ to ensure a Gaussian pull-distribution as defined in [78], are shown below the ${m_\mathrm {VV}}$ plot. |
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Figure 10-c:
Comparison between the fitted result and data distributions of ${m_\mathrm {VV}}$ in the HPHP category. The background shape uncertainty is shown as a red shaded band, and the statistical uncertainties of the data are shown as vertical bars. An example of a signal distribution is overlaid, using an arbitrary normalisation. The corresponding pull distributions (Data-fit)/$\sigma $, where $\sigma = \sqrt {\sigma _\mathrm {data}^2 - \sigma _\mathrm {fit}^2}$ for a bin in ${m_\mathrm {VV}}$ to ensure a Gaussian pull-distribution as defined in [78], are shown below the ${m_\mathrm {VV}}$ plot. |
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Figure 11:
Comparison between the fitted result and data distributions of ${m_\text {jet1}}$ (left), ${m_\text {jet2}}$ (middle), and ${m_\mathrm {VV}}$ (right) in the HPLP category. The background shape uncertainty is shown as a red shaded band, and the statistical uncertainties of the data are shown as vertical bars. An example of a signal distribution is overlaid, using an arbitrary normalisation. The corresponding pull distributions (Data-fit)/$\sigma $, where $\sigma = \sqrt {\sigma _\mathrm {data}^2 - \sigma _\mathrm {fit}^2}$ for a bin in ${m_\mathrm {VV}}$ to ensure a Gaussian pull-distribution as defined in [78], are shown below each ${m_\mathrm {VV}}$ plot. |
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Figure 11-a:
Comparison between the fitted result and data distributions of ${m_\text {jet1}}$ in the HPLP category. The background shape uncertainty is shown as a red shaded band, and the statistical uncertainties of the data are shown as vertical bars. An example of a signal distribution is overlaid, using an arbitrary normalisation. The corresponding pull distributions (Data-fit)/$\sigma $, where $\sigma = \sqrt {\sigma _\mathrm {data}^2 - \sigma _\mathrm {fit}^2}$ for a bin in ${m_\mathrm {VV}}$ to ensure a Gaussian pull-distribution as defined in [78], are shown below the ${m_\mathrm {VV}}$ plot. |
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Figure 11-b:
Comparison between the fitted result and data distributions of ${m_\text {jet2}}$ in the HPLP category. The background shape uncertainty is shown as a red shaded band, and the statistical uncertainties of the data are shown as vertical bars. An example of a signal distribution is overlaid, using an arbitrary normalisation. The corresponding pull distributions (Data-fit)/$\sigma $, where $\sigma = \sqrt {\sigma _\mathrm {data}^2 - \sigma _\mathrm {fit}^2}$ for a bin in ${m_\mathrm {VV}}$ to ensure a Gaussian pull-distribution as defined in [78], are shown below the ${m_\mathrm {VV}}$ plot. |
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Figure 11-c:
Comparison between the fitted result and data distributions of ${m_\mathrm {VV}}$ in the HPLP category. The background shape uncertainty is shown as a red shaded band, and the statistical uncertainties of the data are shown as vertical bars. An example of a signal distribution is overlaid, using an arbitrary normalisation. The corresponding pull distributions (Data-fit)/$\sigma $, where $\sigma = \sqrt {\sigma _\mathrm {data}^2 - \sigma _\mathrm {fit}^2}$ for a bin in ${m_\mathrm {VV}}$ to ensure a Gaussian pull-distribution as defined in [78], are shown below the ${m_\mathrm {VV}}$ plot. |
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Figure 12:
Observed and expected limits obtained with 77.3 fb$^{-1}$ of 13 TeV data after combining categories of all purities for $ {{\mathrm {G}} _{\text {bulk}}} \rightarrow \mathrm{WW}$ (upper left), $ {{\mathrm {G}} _{\text {bulk}}} \rightarrow {\mathrm {Z}} {\mathrm {Z}} $ (upper right), $ {\mathrm {W}'} \rightarrow \mathrm{WZ} $ (lower left), and $ {\mathrm {Z}'} \rightarrow \mathrm{WW}$ (lower right) signals. |
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Figure 12-a:
Observed and expected limits obtained with 77.3 fb$^{-1}$ of 13 TeV data after combining categories of all purities for $ {{\mathrm {G}} _{\text {bulk}}} \rightarrow \mathrm{WW}$ signal. |
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Figure 12-b:
Observed and expected limits obtained with 77.3 fb$^{-1}$ of 13 TeV data after combining categories of all purities for $ {{\mathrm {G}} _{\text {bulk}}} \rightarrow {\mathrm {Z}} {\mathrm {Z}} $ signal. |
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Figure 12-c:
Observed and expected limits obtained with 77.3 fb$^{-1}$ of 13 TeV data after combining categories of all purities for $ {\mathrm {W}'} \rightarrow \mathrm{WZ} $ signal. |
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Figure 12-d:
Observed and expected limits obtained with 77.3 fb$^{-1}$ of 13 TeV data after combining categories of all purities for $ {\mathrm {Z}'} \rightarrow \mathrm{WW}$ signal. |
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Figure 13:
Expected limits for a Bulk $G\rightarrow \mathrm{WW}$ signal using 35.9 fb$^{-1}$ of data collected in 2016 obtained using the multi-dimensional fit method presented here (pink line), compared to the result obtained using previous methods (beige line) [26]. The final limit obtained when combining data collected in 2016 and 2017 is also shown (black dotted line). |
| Tables | |
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Table 1:
W-tagging efficiencies, and jet-mass scale and resolution scale factors as evaluated in the 2016 and 2017 data sets. |
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Table 2:
Summary of the systematic uncertainties and the quantities they affect. Numbers in parentheses correspond to uncertainties for the 2016 analysis if these differ from those for 2017. Dashes indicate shape variations that cannot be described by a single parameter, and are described in the text. |
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Table 3:
Observed and predicted background yields together with post-fit uncertainties in the two purity categories. |
| Summary |
| A search is presented for resonances with masses above 1.2 TeV that decay to WW, ZZ, or WZ. Each boson decays hadronically into one large-radius jet, resulting in dijet final states. This method yields improvements in sensitivity of up to 30% with respect to previous search methods used in CMS [26]. The new technique allows for placing additional constraints on systematic uncertainties concerning the signal through a measurement of the standard model W/Z+jets background. Hadronic W and Z boson decays are identified by requiring a jet with mass compatible with the W or Z boson mass, respectively. Additional information from jet substructure is used to reduce the background from multijet production. No evidence is found for a signal, and upper limits on the resonance production cross section are set as functions of the resonance mass. The results are interpreted within bulk graviton models and as W' and Z' resonances within the heavy vector triplet framework. For the heavy vector triplet model B, we exclude at 95% confidence level W' and Z' spin-1 resonances with masses below 3.8 and 3.5 TeV, respectively. In the narrow-width bulk graviton model, upper limits on the production cross sections for ${\mathrm{G}_{\text{bulk}}} \rightarrow \mathrm{W}\mathrm{W}$ are set in the range from 20 fb for a resonance mass of 1.2 TeV, to the most stringent limit of 0.2 fb for high resonance masses of 5.2 TeV TeV. In the case of ${\mathrm{G}_{\text{bulk}}} \rightarrow \mathrm{Z}\mathrm{Z}$, upper limits in the cross section are between 27 and 0.2 fb for bulk graviton masses between 1.2 and 5.2 TeV, respectively. In the narrow-width bulk graviton model, upper limits on the production cross sections are set in the range from 20 fb for a resonance mass of 1.2 TeV, to the most stringent limit of 0.2 fb for a resonance mass of 5.2 TeV TeV for ${\mathrm{G}_{\text{bulk}}} \rightarrow \mathrm{W}\mathrm{W}$. |
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