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 √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. | ||
Links:
CDS record (PDF) ;
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These preliminary results are superseded in this paper, EPJC 80 (2020) 237. The superseded preliminary plots can be found here. |
Figures | |
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Figure 1:
The τ21 profile dependence on ρ′=log(mjet2/pT/μ) (left). A fit to the linear part of the spectrum yields the slope M=−0.080, which is used to define the mass- and pT -decorrelated variable τDDT21=τ21−M×ρ′. The τDDT21 profile versus ρ′ is shown on the right. |
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Figure 1-a:
The τ21 profile dependence on ρ′=log(mjet2/pT/μ). A fit to the linear part of the spectrum yields the slope M=−0.080, which is used to define the mass- and pT -decorrelated variable τDDT21=τ21−M×ρ′. |
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Figure 1-b:
The τDDT21 profile versus ρ′ is shown. |
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Figure 2:
Left: Performance of τ21 and τDDT21 in the background-signal efficiency plane. Right: Distribution of τ21 and τDDT21 for W-jets and quark or gluon jets from QCD multijet events. |
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Figure 2-a:
Performance of τ21 and τDDT21 in the background-signal efficiency plane. |
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Figure 2-b:
Distribution of τ21 and τDDT21 for W-jets and quark or gluon jets from QCD multijet events. |
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Figure 3:
Jet mass (upper left) and τDDT21 (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 τDDT21 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 τDDT21 is applied. |
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Figure 3-b:
τDDT21 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 τDDT21 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 τDDT21 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 τDDT21< 0.43 selection in the t¯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 τDDT21< 0.43 selection in the t¯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 τDDT21< 0.43 selection in the t¯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 τDDT21< 0.43 selection in the t¯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 τDDT21< 0.43 selection in the t¯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 mVV (left) and mjet2 (right) signal shapes extracted from the parametrisation. Shown here is a Gbulk decaying to WW. The mjet2 distribution is for a jet in the HPHP category. |
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Figure 6-a:
Final mVV signal shape extracted from the parametrisation. Shown here is a Gbulk decaying to WW. The mjet2 distribution is for a jet in the HPHP category. |
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Figure 6-b:
Final mjet2 signal shape extracted from the parametrisation. Shown here is a Gbulk decaying to WW. The mjet2 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 mX, obtained from the mean and σ 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 mX, obtained from the mean and σ 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 mX, obtained from the mean and σ 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 mX for signal models with a Gbulk decaying to WW, Gbulk 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 mjet1 (left), mjet2 (middle), and mVV (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)/σ, where σ=√σ2data−σ2fit for a bin in mVV to ensure a Gaussian pull-distribution as defined in [78], are shown below each mVV plot. |
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Figure 10-a:
Comparison between the fitted result and data distributions of mjet1 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)/σ, where σ=√σ2data−σ2fit for a bin in mVV to ensure a Gaussian pull-distribution as defined in [78], are shown below the mVV plot. |
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Figure 10-b:
Comparison between the fitted result and data distributions of mjet2 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)/σ, where σ=√σ2data−σ2fit for a bin in mVV to ensure a Gaussian pull-distribution as defined in [78], are shown below the mVV plot. |
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Figure 10-c:
Comparison between the fitted result and data distributions of mVV 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)/σ, where σ=√σ2data−σ2fit for a bin in mVV to ensure a Gaussian pull-distribution as defined in [78], are shown below the mVV plot. |
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Figure 11:
Comparison between the fitted result and data distributions of mjet1 (left), mjet2 (middle), and mVV (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)/σ, where σ=√σ2data−σ2fit for a bin in mVV to ensure a Gaussian pull-distribution as defined in [78], are shown below each mVV plot. |
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Figure 11-a:
Comparison between the fitted result and data distributions of mjet1 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)/σ, where σ=√σ2data−σ2fit for a bin in mVV to ensure a Gaussian pull-distribution as defined in [78], are shown below the mVV plot. |
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Figure 11-b:
Comparison between the fitted result and data distributions of mjet2 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)/σ, where σ=√σ2data−σ2fit for a bin in mVV to ensure a Gaussian pull-distribution as defined in [78], are shown below the mVV plot. |
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Figure 11-c:
Comparison between the fitted result and data distributions of mVV 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)/σ, where σ=√σ2data−σ2fit for a bin in mVV to ensure a Gaussian pull-distribution as defined in [78], are shown below the mVV 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 Gbulk→WW (upper left), Gbulk→ZZ (upper right), W′→WZ (lower left), and Z′→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 Gbulk→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 Gbulk→ZZ 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 W′→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 Z′→WW signal. |
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Figure 13:
Expected limits for a Bulk G→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 Gbulk→WW 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 Gbulk→ZZ, 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 Gbulk→WW. |
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Compact Muon Solenoid LHC, CERN |
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