A search for new resonances in multiple final states with a high transverse momentum $Z$ boson in $\sqrt{s}=13$ TeV $pp$ collisions with the ATLAS detector

The ATLAS collaboration
JHEP 06 (2023) 036, 2023.

Abstract (data abstract)
A search for new resonances in multiple final states with a high transverse momentum Z boson in $\sqrt{s}=$13 TeV pp collisions with the ATLAS detector A generic search for resonances is performed with events containing a Z boson with transverse momentum greater than 100GeV, decaying to $e^{+}e^{-}$ or $\mu^{+}\mu^{-}$. The analysed data collected with the ATLAS detector in proton–proton collisions at a center-of-mass energy of 13 TeV at the Large Hadron Collider corresponds to an integrated luminosity of 139 $fb^{-1}$. Two invariant mass distributions are examined for a localised excess with respect to the Standard Model background in six independent event categories and their combination to increase the sensitivity. No significant excess is observed. Exclusion limits at 95% confidence level are derived for two cases: a model independent interpretation of Gaussian-shaped resonances with the mass width between 3% and 10% of the resonance mass, and a specific model of the heavy vector triplet with the decay mode $𝑊^{\prime} \to ZW \to \ell\ell qq$.

  • Figure3_LeadJ_X_BH

    Figure3 (a)

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    Results of applying the BH algorithm to the mass spectra in the leading small-R jet category, using the fitted background...

  • Figure3_LeadB_X_BH

    Figure3 (b)

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    Results of applying the BH algorithm to the mass spectra in the leading bjet category, using the fitted background estimations...

  • Figure3_LeadFatJ_X_BH

    Figure3 (c)

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    Results of applying the BH algorithm to the mass spectra in the leading large-R jet category, using the fitted background...

  • Figure3_LeadP_X_BH

    Figure3 (d)

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    Results of applying the BH algorithm to the mass spectra in the leading photon category, using the fitted background estimations...

  • Figure3_LeadE_X_BH

    Figure3 (e)

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    Results of applying the BH algorithm to the mass spectra in the leading electron category, using the fitted background estimations...

  • Figure3_LeadM_X_BH

    Figure3 (f)

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    Results of applying the BH algorithm to the mass spectra in the leading muon category, using the fitted background estimations...

  • Figure4_LeadJ_ZX_BH

    Figure4 (a)

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    Results of applying the BH algorithm to the mass spectra in the leading small-R jet category, using the fitted background...

  • Figure4_LeadB_ZX_BH

    Figure4 (b)

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    Results of applying the BH algorithm to the mass spectra in the leading bjet category, using the fitted background estimations...

  • Figure4_LeadFatJ_ZX_BH

    Figure4 (c)

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    Results of applying the BH algorithm to the mass spectra in the leading large-R jet category, using the fitted background...

  • Figure4_LeadP_ZX_BH

    Figure4 (d)

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    Results of applying the BH algorithm to the mass spectra in the leading photon category, using the fitted background estimations...

  • Figure4_LeadE_ZX_BH

    Figure4 (e)

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    Results of applying the BH algorithm to the mass spectra in the leading electron category, using the fitted background estimations...

  • Figure4_LeadM_ZX_BH

    Figure4 (f)

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    Results of applying the BH algorithm to the mass spectra in the leading muon category, using the fitted background estimations...

  • Figure5_combined_X_BH

    Figure5 (a)

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    Results of applying the BH algorithm to the mass spectra in the inclusive category, using the fitted background estimations from...

  • Figure5_combined_ZX_BH

    Figure5 (b)

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    Results of applying the BH algorithm to the mass spectra in the inclusive category, using the fitted background estimations from...

  • Figure6_LeadJ_X_XS

    Figure6 (a)

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    Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a...

  • Figure6_LeadB_X_XS

    Figure6 (b)

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    Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a...

  • Figure6_LeadFatJ_X_XS

    Figure6 (c)

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    Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a...

  • Figure6_LeadP_X_XS

    Figure6 (d)

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    Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a...

  • Figure6_LeadE_X_XS

    Figure6 (e)

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    Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a...

  • Figure6_LeadM_X_XS

    Figure6 (f)

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    Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a...

  • Figure7_LeadJ_ZX_XS

    Figure7 (a)

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    Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a...

  • Figure7_LeadB_ZX_XS

    Figure7 (b)

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    Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a...

  • Figure7_LeadFatJ_ZX_XS

    Figure7 (c)

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    Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a...

  • Figure7_LeadP_ZX_XS

    Figure7 (d)

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    Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a...

  • Figure7_LeadE_ZX_XS

    Figure7 (e)

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    Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a...

  • Figure7_LeadM_ZX_XS

    Figure7 (f)

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    Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a...

  • Figure8_combined_X_XS

    Figure8 (a)

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    Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a...

  • Figure8_combined_ZX_XS

    Figure8 (b)

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    Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a...

  • Figure9_LeadJ_X_obs

    Figure9 (a)

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    Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped...

  • Figure9_LeadB_X_obs

    Figure9 (b)

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    Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped...

  • Figure9_LeadFatJ_X_obs

    Figure9 (c)

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    Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped...

  • Figure9_LeadP_X_obs

    Figure9 (d)

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    Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped...

  • Figure9_LeadE_X_obs

    Figure9 (e)

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    Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped...

  • Figure9_LeadM_X_obs

    Figure9 (f)

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    Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped...

  • Figure10_LeadJ_ZX_obs

    Figure10 (a)

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    Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped...

  • Figure10_LeadB_ZX_obs

    Figure10 (b)

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    Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped...

  • Figure10_LeadFatJ_ZX_obs

    Figure10 (c)

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    Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped...

  • Figure10_LeadP_ZX_obs

    Figure10 (d)

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    Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped...

  • Figure10_LeadE_ZX_obs

    Figure10 (e)

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    Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped...

  • Figure10_LeadM_ZX_obs

    Figure10 (f)

    10.17182/hepdata.132793.v1/t40

    Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped...

  • Figure11_combined_X_obs

    Figure11 (a)

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    Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped...

  • Figure11_combined_ZX_obs

    Figure11 (b)

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    Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped...

  • Figure12_comb_eff_hvt

    Figure12

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    Acceptance times efficiency in an HVT model as a function of mass in the leading large-R jet category

  • Figure13_GeneralZplusX_Gaus_LeadFatJ_ZXmass_HVT_limit_xs

    Figure13

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    Upper limits at 95% CL on the cross section times the branching fraction($W^{\prime} \to ZW$) for the HVT signal as...

  • Figure14_truth_eff

    Figure14 (a)

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    Comparison of the identification efficiencies using standard and merged-ee reconstruction as a function of true PT(Z)

  • Figure14_bkg_rejection_as_signal_eff

    Figure14 (b)

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    Background rejection factor as a function of signal efficiency. The red curve shows the BDT performance whereas the blue curve...

  • Figure15_leadj_mx_2500_step1

    Figure15 (a)

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    BH p-values of the 100 pseudo-experiments as a function of in the leading small-R jet category for an injected Gaussian-shaped...

  • Figure15_leadj_mx_2500_step2

    Figure15 (b)

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    BH p-values of the 100 pseudo-experiments as a function of in the leading small-R jet category for an injected Gaussian-shaped...

  • Figure16_leadj_mx_5sig

    Figure16 (a)

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    Fractions of pseudo-experiments in which the detected BH interval agrees with the injected mass point and the BH p-value is...

  • Figure16_leadj_mzx_5sig

    Figure16 (b)

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    Fractions of pseudo-experiments in which the detected BH interval agrees with the injected mass point and the BH p-value is...

  • Figure17_leadj_mzx_width3_lowlimit

    Figure17 (a)

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    Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative...

  • Figure17_leadj_mzx_width3_highlimit

    Figure17 (b)

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    Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative...

  • Figure17_leadj_mzx_width5_lowlimit

    Figure17 (c)

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    Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative...

  • Figure17_leadj_mzx_width5_highlimit

    Figure17 (d)

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    Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative...

  • Figure17_leadj_mzx_width10_lowlimit

    Figure17 (e)

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    Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative...

  • Figure17_leadj_mzx_width10_highlimit

    Figure17 (f)

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    Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative...

  • Table 1: data yields

    Table 1

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    Data yields of the six event categories in the $Z\to e^+e^-$ and $\mu^+\mu^-$ decay channels. The merged-$e^+e^-$ events are included...

  • Table 2: fit ranges and functions

    Table 2

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    A list of mass spectra, event categories and their corresponding fit ranges, functional forms, numbers of free parameters and global...

  • Table 3: sensitive mass ranges and p-values

    Table 3

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    A list of mass spectra, event categories and their corresponding signal-sensitive mass ranges. The initial BH $p$-value is obtained by...

  • Table 4: Pseudo experiment table

    Table 4

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    A list of mass spectra, event categories, relative width values of Gaussian-shaped signals and limit-sensitive mass ranges and fractions, corresponding...

  • Table 5: cutflow of HVT model A signals

    Table 5

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    Cutflow of HVT model $A$ signals ($W^\prime \to ZW \to \ell\ell qq$) with $m_{W^\prime} = 1$ TeV and $m_{W^\prime} =...

  • Table 6: acceptance times efficiency

    Table 6

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    Acceptance times efficiency ($\mathcal{A} \times \epsilon$) values in % in the dominant event category for $p^Z_{\mathrm{T}} > 100$ GeV in...

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