Two-dimensional distribution of the invariant mass $m_{DV}$ and the track multiplicity in the High-pT jet SR for observed data events

Two-dimensional distribution of the invariant mass $m_{DV}$ and the track multiplicity in the High-pT jet SR for expected signal events in the strong gluino pair pair production model with m(gluino)=1.8 TeV, m(chi0)=0.2 TeV, tau(chi0)=0.1 ns

Two-dimensional distribution of the invariant mass $m_{DV}$ and the track multiplicity in the Trackless jet SR for observed data events

Two-dimensional distribution of the invariant mass $m_{DV}$ and the track multiplicity in the Trackless jet SR for expected signal events in the electroweak pair production model

Expected exclusion limits at 95% CL on the lifetime and mass of the neutralino in electroweakino pair production models

Expected (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in electroweakino pair production models

Expected (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in electroweakino pair production models

Observed exclusion limits at 95% CL on the lifetime and mass of the neutralino in electroweakino pair production models

Observed (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in electroweakino pair production models

Observed (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in electroweakino pair production models

Expected exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.4 TeV

Expected (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.4 TeV

Expected (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.4 TeV

Observed exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.4 TeV

Observed (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.4 TeV

Observed (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.4 TeV

Exclusion limits at 95% CL on the production cross section in the electroweak pair production model.

Exclusion limits at 95% CL on the production cross section in the strong gluino pair production models and m(gluino)=2.4 TeV

Expected exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.0 TeV

Expected (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.0 TeV

Expected (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.0 TeV

Observed exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.0 TeV

Observed (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.0 TeV

Observed (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.0 TeV

Expected exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.2 TeV

Expected (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.2 TeV

Expected (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.2 TeV

Observed exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.2 TeV

Observed (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.2 TeV

Observed (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the neutralino in strong gluino pair production models and m(gluino)=2.2 TeV

Expected exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=50 GeV

Expected (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=50 GeV

Expected (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=50 GeV

Observed exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=50 GeV

Observed (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=50 GeV

Observed (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=50 GeV

Expected exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=450 GeV

Expected (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=450 GeV

Expected (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=450 GeV

Observed exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=450 GeV

Observed (+1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=450 GeV

Observed (-1 sigma) exclusion limits at 95% CL on the lifetime and mass of the gluino in strong gluino pair production models and m(chi0)=450 GeV

Expected exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.01 ns

Expected (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.01 ns

Expected (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.01 ns

Observed exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.01 ns

Observed (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.01 ns

Observed (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.01 ns

Expected exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.1 ns

Expected (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.1 ns

Expected (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.1 ns

Observed exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.1 ns

Observed (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.1 ns

Observed (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=0.1 ns

Expected exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=1 ns

Expected (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=1 ns

Expected (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=1 ns

Observed exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=1 ns

Observed (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=1 ns

Observed (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=1 ns

Expected exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=10 ns

Expected (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=10 ns

Expected (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=10 ns

Observed exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=10 ns

Observed (+1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=10 ns

Observed (-1 sigma) exclusion limits at 95% CL on the mass of the gluino and neutralino in strong gluino pair production models and tau(chi0)=10 ns

Exclusion limits at 95% CL on the production cross section in the strong gluino pair production models and m($ ilde{\chi}^0_1$)=1.25 TeV

Acceptance cutflow for the High-pT SR for representative points in the strong gluino pair production model. See additional resources for more information.

Acceptance cutflow for the Trackless SR for representative points in the electroweak pair production model. See additional resources for more information.

Acceptance cutflow for the Trackless SR for representative points in the electroweak pair production model with heavy-flavor quarks final state. See additional resources for more information.

Acceptance cutflow for the High-pT SR for representative points in the electroweak pair production model with heavy-flavor quarks final state. See additional resources for more information.

Reinterpretation Material: Event-level Efficiency for HighPt SR selections, R < 1150 mm

Reinterpretation Material: Event-level Efficiency for HighPt SR selections, R [1150, 3870] mm

Reinterpretation Material: Event-level Efficiency for HighPt SR selections, R > 3870 mm

Reinterpretation Material: Event-level Efficiency for Trackless SR selections, R < 1150 mm

Reinterpretation Material: Event-level Efficiency for Trackless SR selections, R [1150, 3870] mm

Reinterpretation Material: Event-level Efficiency for Trackless SR selections, R > 3870 mm

Reinterpretation Material: Vertex-level Efficiency for R < 22 mm

Reinterpretation Material: Vertex-level Efficiency for R [22, 25] mm

Reinterpretation Material: Vertex-level Efficiency for R [25, 29] mm

Reinterpretation Material: Vertex-level Efficiency for R [29, 38] mm

Reinterpretation Material: Vertex-level Efficiency for R [38, 46] mm

Reinterpretation Material: Vertex-level Efficiency for R [46, 73] mm

Reinterpretation Material: Vertex-level Efficiency for R [73, 84] mm

Reinterpretation Material: Vertex-level Efficiency for R [84, 111] mm

Reinterpretation Material: Vertex-level Efficiency for R [111, 120] mm

Reinterpretation Material: Vertex-level Efficiency for R [120, 145] mm

Reinterpretation Material: Vertex-level Efficiency for R [145, 180] mm

Reinterpretation Material: Vertex-level Efficiency for R [180, 300] mm

Cutflow (acceptance x efficiency) for the High-pT SR for representative points in the strong gluino pair production model. See additional resources for more information.

Cutflow (acceptance x efficiency) for the Trackless SR for representative points in the electroweak pair production model. See additional resources for more information.

Cutflow (acceptance x efficiency) for the Trackless SR for representative points in the electroweak pair production model with heavy-flavor quarks. See additional resources for more information.

Cutflow (acceptance x efficiency) for the High-pT SR for representative points in the electroweak pair production model with heavy-flavor quarks. See additional resources for more information.

Bayesian upper exclusion limits on the ratio $g_{W'}/g_{W}$ for an SSM-like W$\prime$ boson are shown. The unity coupling ratio (blue dotted curve) corresponds to the SSM common benchmark. The 68 and 95% quantiles of the limits are represented by the green and yellow bands, respectively.

Bayesian lower exclusion limits on the NUGIM G(221) mixing angle $\cot(\theta_{E})$ are shown as a function of the W$\prime$ boson mass. The theoretically excluded region is shaded in grey. The 68 and 95% quantiles of the limits are represented by the green and yellow bands, respectively.

Bayesian upper exclusion limits at 95% CL on the product of the production cross section and branching fraction of a QBH in an associated $ au$ lepton and neutrino final state. Minimum threshold masses $m_{th}$ of up to 6.6 TeV are excluded at 95% CL. The observed limit (solid line) is obtained from the intersection with the LO QBH cross section (blue dotted curve). The 68 and 95% quantiles of the limits are represented by the green and yellow bands, respectively.

Bayesian upper limits at 95% CL on the cross section of the process $pp\rightarrow\tau\nu$ mediated via LQ exchange in the t-channel. The inner (green) band and the outer (yellow) band indicate the regions containing 68 and 95%, respectively, of the distribution of limits expected under the background-only hypothesis. The predicted LQ cross section at LO in the three coupling benchmark scenarios is depicted in different colors for $g_{U}=1$. The uncertainty bandy correspond to the sum in quadrature of PDF and scale variations. The first benchmark scenario considers only couplings to left-handed SM fermions (i.e. $\beta_{\text{R}}^{ij}=0$) and is referred to as "best fit LH". The second benchmark, referred to as "best fit LH+RH", considers $|\beta_{\text{R}}^{\text{b}\tau}|=1$ and all other $\beta_{\text{R}}^{ij}=0$. In the third "democratic" benchmark, equal couplings only to LH fermions are assumed, i.e. $\beta_{\text{L}}^{ij}=1$ and $\beta_{\text{R}}^{ij}=0$ for all $i$ and $j$.

Expected and observed lower limits of the LQ mass as a function of the coupling $g_{U}$ in the LH scenario. The blue band shows the 68% and 95% regions of $g_{U}$ preferred by the fit to the b anomalies data.

Expected and observed lower limits of the LQ mass as a function of the coupling $g_{U}$ in the LH+RH scenario. The blue band shows the 68% and 95% regions of $g_{U}$ preferred by the fit to the b anomalies data.

Expected and observed lower limits of the LQ mass as a function of the coupling $g_{U}$ in the democratic scenario. The blue band shows the 68% and 95% regions of $g_{U}$ preferred by the fit to the b anomalies data.

Bayesian upper exclusion limits at 95% CL on each of the Wilson coefficients described by the EFT model based on 2016-2018 data. The three different coupling types represent a left-handed vector coupling ($\epsilon^{cb}_{L}$), tensor-like coupling ($\epsilon^{cb}_{T}$), and scalar-tensor-like coupling ($\epsilon^{cb}_{S_{L}}$). The 68 and 95% quantiles of the limits are represented by the green and yellow bands, respectively.

Summary of exclusion limits (expected and observed) calculated at 95% CL for full Run-2 CMS data.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "year_binnumber", following the binning from Figure 4.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "year_binnumber", following the binning used in Figure 4.

Predicted signal yields for the 2017 data-taking period, corresponding to 41.3 fb$^{-1}$, after the application of each search requirement (cumulative) for various signal hypothesis. The requirements listed are presented as total efficiencies w.r.t. the previous selection step.

Unfolded $M(l^{-}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64822.9 $\pm$ 254.6, NPowHeg truth =634276.

Unfolded dR distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64273.2 $\pm$ 253.5, NPowHeg truth =338671.

Unfolded dR distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 65362.4 $\pm$ 255.7 , NPowHeg truth =634214.

Unfolded $P_{T}^{\gamma}$ distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 63486.8 $\pm$ 252.0, NPowHeg truth =333348.

Unfolded $P_{T}^{\gamma}$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64432.6 $\pm$ 253.8, NPowHeg truth =624059.

Unfolded $M(l^{+}\gamma)$ distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57095.9 $\pm$ 254 , NPowHeg truth =301666, N Sherpa truth =76327.

Unfolded $M(l^{-}\gamma)$ distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57130 $\pm$ 254 , NPowHeg truth =301666, N Sherpa truth =763273.

Unfolded $M(l^{+}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58310.9 $\pm$ 245, NPowHeg truth =564672, N Sherpa truth =814016.

Unfolded $M(l^{-}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58320.9 $\pm$ 245, NPowHeg truth =564672, N Sherpa truth =814016.

Unfolded dR distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57705.4 $\pm$ 254 , NPowHeg truth =301655, N Sherpa truth =763259.

Unfolded dR distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58869.2 $\pm$ 256, NPowHeg truth =564660, NS herpa truth =813995.

Unfolded $P_{T}^{\gamma}$ distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57358.6 $\pm$ 254 , NPowHeg truth =298662, N Sherpa truth =756609.

Unfolded $P_{T}^{\gamma}$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58226.4 $\pm$ 254, NPowHeg truth =558949, N Sherpa truth =806988.

Unfolded $M(l^{+}\gamma)$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 63717.4 $\pm$ 252.4, NPowHeg truth =338714.

Unfolded $M(l^{-}\gamma)$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 63855.8 $\pm$ 252.7 , NPowHeg truth =338708.

Unfolded $M(l^{+}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64809.8 $\pm$ 254.6, NPowHeg truth =634285.

Unfolded $M(l^{-}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64822.9 $\pm$ 254.6, NPowHeg truth =634276.

Unfolded dR distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64273.2 $\pm$ 253.5, NPowHeg truth =338671.

Unfolded dR distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 65362.4 $\pm$ 255.7 , NPowHeg truth =634214.

Unfolded $P_{T}^{\gamma}$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 63486.8 $\pm$ 252.0, NPowHeg truth =333348.

Unfolded $P_{T}^{\gamma}$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64432.6 $\pm$ 253.8, NPowHeg truth =624059.

Unfolded $M(l^{+}\gamma)$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57095.9 $\pm$ 254 , NPowHeg truth =301666, N Sherpa truth =76327.

Unfolded $M(l^{-}\gamma)$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57130 $\pm$ 254 , NPowHeg truth =301666, N Sherpa truth =763273.

Unfolded $M(l^{+}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58310.9 $\pm$ 245, NPowHeg truth =564672, N Sherpa truth =814016.

Unfolded $M(l^{-}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58320.9 $\pm$ 245, NPowHeg truth =564672, N Sherpa truth =814016.

Unfolded dR distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57705.4 $\pm$ 254 , NPowHeg truth =301655, N Sherpa truth =763259.

Unfolded dR distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58869.2 $\pm$ 256, NPowHeg truth =564660, NS herpa truth =813995.

Unfolded $P_{T}^{\gamma}$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57358.6 $\pm$ 254 , NPowHeg truth =298662, N Sherpa truth =756609.

Unfolded $P_{T}^{\gamma}$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58226.4 $\pm$ 254, NPowHeg truth =558949, N Sherpa truth =806988.

Truth $P_{T}^{Z}$ distribution after reweghting to data.

Combined Covariance Matrix for Unfolded $M(l^{+}\gamma)$ distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 63717.4 $\pm$ 252.4, NPowHeg truth =338714.

Combined Covariance Matrix for Unfolded $M(l^{-}\gamma)$ distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 63855.8 $\pm$ 252.7 , NPowHeg truth =338708.

Combined Covariance Matrix for Unfolded $M(l^{+}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64809.8 $\pm$ 254.6, NPowHeg truth =634285.

Combined Covariance Matrix for Unfolded $M(l^{-}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64822.9 $\pm$ 254.6, NPowHeg truth =634276.

Combined Covariance Matrix for Unfolded dR distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64273.2 $\pm$ 253.5, NPowHeg truth =338671.

Combined Covariance Matrix for Unfolded dR distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 65362.4 $\pm$ 255.7 , NPowHeg truth =634214.

Combined Covariance Matrix for Unfolded $P_{T}^{\gamma}$ distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 63486.8 $\pm$ 252.0, NPowHeg truth =333348.

Combined Covariance Matrix for Unfolded $P_{T}^{\gamma}$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64432.6 $\pm$ 253.8, NPowHeg truth =624059.

Combined Covariance Matrix for Unfolded $M(l^{+}\gamma)$ distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57095.9 $\pm$ 254 , NPowHeg truth =301666, N Sherpa truth =76327.

Combined Covariance Matrix for Unfolded $M(l^{-}\gamma)$ distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57130 $\pm$ 254 , NPowHeg truth =301666, N Sherpa truth =763273.

Combined Covariance Matrix for Unfolded $M(l^{+}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58310.9 $\pm$ 245, NPowHeg truth =564672, N Sherpa truth =814016.

Combined Covariance Matrix for Unfolded $M(l^{-}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58320.9 $\pm$ 245, NPowHeg truth =564672, N Sherpa truth =814016.

Combined Covariance Matrix for Unfolded dR distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57705.4 $\pm$ 254 , NPowHeg truth =301655, N Sherpa truth =763259.

Combined Covariance Matrix for Unfolded dR distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58869.2 $\pm$ 256, NPowHeg truth =564660, NS herpa truth =813995.

Combined Covariance Matrix for Unfolded $P_{T}^{\gamma}$ distribution for $Z \to ee\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57358.6 $\pm$ 254 , NPowHeg truth =298662, N Sherpa truth =756609.

Combined Covariance Matrix for Unfolded $P_{T}^{\gamma}$ distribution for $Z \to \mu\mu\gamma$ process with dressed leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58226.4 $\pm$ 254, NPowHeg truth =558949, N Sherpa truth =806988.

Combined Covariance Matrix for Unfolded $M(l^{+}\gamma)$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 63717.4 $\pm$ 252.4, NPowHeg truth =338714.

Combined Covariance Matrix for Unfolded $M(l^{-}\gamma)$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 63855.8 $\pm$ 252.7 , NPowHeg truth =338708.

Combined Covariance Matrix for Unfolded $M(l^{+}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64809.8 $\pm$ 254.6, NPowHeg truth =634285.

Combined Covariance Matrix for Unfolded $M(l^{-}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64822.9 $\pm$ 254.6, NPowHeg truth =634276.

Combined Covariance Matrix for Unfolded dR distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64273.2 $\pm$ 253.5, NPowHeg truth =338671.

Combined Covariance Matrix for Unfolded dR distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 65362.4 $\pm$ 255.7 , NPowHeg truth =634214.

Combined Covariance Matrix for Unfolded $P_{T}^{\gamma}$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 63486.8 $\pm$ 252.0, NPowHeg truth =333348.

Combined Covariance Matrix for Unfolded $P_{T}^{\gamma}$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>20$ GeV. Nexp.un f. = 64432.6 $\pm$ 253.8, NPowHeg truth =624059.

Combined Covariance Matrix for Unfolded $M(l^{+}\gamma)$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57095.9 $\pm$ 254 , NPowHeg truth =301666, N Sherpa truth =76327.

Combined Covariance Matrix for Unfolded $M(l^{-}\gamma)$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57130 $\pm$ 254 , NPowHeg truth =301666, N Sherpa truth =763273.

Combined Covariance Matrix for Unfolded $M(l^{+}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58310.9 $\pm$ 245, NPowHeg truth =564672, N Sherpa truth =814016.

Combined Covariance Matrix for Unfolded $M(l^{-}\gamma)$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58320.9 $\pm$ 245, NPowHeg truth =564672, N Sherpa truth =814016.

Combined Covariance Matrix for Unfolded dR distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57705.4 $\pm$ 254 , NPowHeg truth =301655, N Sherpa truth =763259.

Combined Covariance Matrix for Unfolded dR distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58869.2 $\pm$ 256, NPowHeg truth =564660, NS herpa truth =813995.

Combined Covariance Matrix for Unfolded $P_{T}^{\gamma}$ distribution for $Z \to ee\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 57358.6 $\pm$ 254 , NPowHeg truth =298662, N Sherpa truth =756609.

Combined Covariance Matrix for Unfolded $P_{T}^{\gamma}$ distribution for $Z \to \mu\mu\gamma$ process with bare leptons and bkg subtraction. $M_{ll}>45$ GeV. Nexp.un f. = 58226.4 $\pm$ 254, NPowHeg truth =558949, N Sherpa truth =806988.

Unfolded $M(l^{+}\gamma\gamma)$ distribution for $Z \to ll\gamma\gamma$ process with dressed leptons.

Unfolded $M(l^{+}\gamma\gamma)$ distribution for $Z \to ll\gamma\gamma$ process with bare leptons.

Unfolded dR distribution for $Z \to ll\gamma\gamma$ process with dressed leptons for leading photon.

Unfolded dR distribution for $Z \to ll\gamma\gamma$ process with bare leptons for leading photon.

Unfolded dR distribution for $Z \to ll\gamma\gamma$ process with dressed leptons for second photon.

Unfolded dR distribution for $Z \to ll\gamma\gamma$ process with bare leptons for second photon.

Unfolded dRgg distribution for $Z \to ll\gamma\gamma$ process with dressed leptons.

Unfolded dRgg distribution for $Z \to ll\gamma\gamma$ process with bare leptons.

Unfolded $P_{T}^{\gamma 1}$ distribution for $Z \to ll\gamma\gamma$ process with dressed leptons for leading photon.

Unfolded $P_{T}^{\gamma 1}$ distribution for $Z \to ll\gamma\gamma$ process with bare leptons for leading photon.

Unfolded $P_{T}^{\gamma 2}$ distribution for $Z \to ll\gamma\gamma$ process with dressed leptons for second photon.

Unfolded $P_{T}^{\gamma 2}$ distribution for $Z \to ll\gamma\gamma$ process with bare leptons for second photon.

Efficiency corrected cumulants of net-charge distributions as a function of $\langle N_{part} \rangle$ from Au+Au collisions at different collision energies.

$\langle N_{part} \rangle$ dependence of efficiency corrected $\mu / \sigma^2$ of net-charge distributions for Au+Au collisions at different collision energies.

$\langle N_{part} \rangle$ dependence of efficiency corrected $S \sigma$ of net-charge distributions for Au+Au collisions at different collision energies.

$\langle N_{part} \rangle$ dependence of efficiency corrected $\kappa \sigma^2$ of net-charge distributions for Au+Au collisions at different collision energies.

$\langle N_{part} \rangle$ dependence of efficiency corrected $S \sigma^3 / \mu$ of net-charge distributions for Au+Au collisions at different collision energies.

The energy dependence of efficiency corrected $\mu / \sigma^2$, $S \sigma$, $\kappa \sigma^2$, and $S \sigma^3 / \mu$ of netcharge distributions for central (0%–5%) Au+Au collisions.

The energy dependence of the chemical freeze-out parameter $\mu_B$.

Away-side jet widths from a Gaussian fit by $h^{\pm}$ partner momentum for various $\pi^0$ trigger momenta in Au+Au collisions.

Away-side $I_{AA}$ for the entire away-side $|\Delta \phi - \pi| < \pi /2$ selection vs. $h^{\pm}$ partner momentum for various $\pi^0$ trigger momenta. A point-to-point uncorrelated 6% normalization uncertainty (mainly due to efficiency corrections) applies to all measurements.

Away-side $I_{AA}$ for the entire away-side $|\Delta \phi - \pi| < \pi /2$ selection vs. $h^{\pm}$ partner momentum for various $\pi^0$ trigger momenta. A point-to-point uncorrelated 6% normalization uncertainty (mainly due to efficiency corrections) applies to all measurements.

Away-side $I_{AA}$ for a narrow “head” $|\Delta \phi - \pi| < \pi /6$ selection vs. $h^{\pm}$ partner momentum for various $\pi^0$ trigger momenta. A point-to-point uncorrelated 6% normalization uncertainty (mainly due to efficiency corrections) applies to all measurements.

Away-side $I_{AA}$ for a narrow “head” $|\Delta \phi - \pi| < \pi /6$ selection vs. $h^{\pm}$ partner momentum for various $\pi^0$ trigger momenta. A point-to-point uncorrelated 6% normalization uncertainty (mainly due to efficiency corrections) applies to all measurements.

Supplemental near-side jet widths from a Gaussian fit by $h^{\pm}$ partner momentum for various $\pi^0$ trigger momenta in $p+p$ collisions.

Supplemental near-side jet widths from a Gaussian fit by $h^{\pm}$ partner momentum for various $\pi^0$ trigger momenta in Au+Au collisions.

Supplemental near-side jet widths from a Gaussian fit by $h^{\pm}$ partner momentum for various $\pi^0$ trigger momenta in Au+Au collisions.

Supplemental away-side $I_{AA}$ for $|\Delta \phi - \pi| < \pi /3$ selection vs. $h^{\pm}$ partner momentum for various $\pi^0$ trigger momenta. A point-to-point uncorrelated 6% normalization uncertainty (mainly due to efficiency corrections) applies to all measurements.

Supplemental away-side $I_{AA}$ for $|\Delta \phi - \pi| < \pi /3$ selection vs. $h^{\pm}$ partner momentum for various $\pi^0$ trigger momenta. A point-to-point uncorrelated 6% normalization uncertainty (mainly due to efficiency corrections) applies to all measurements.

$p+p$ yield vs $p_T^a$ supplemental tables.

Au+Au yield vs $p_T^a$ supplemental tables.

Au+Au yield vs $p_T^a$ supplemental tables.

Total efficiencies for the selection of signal events after categorization and application of the tighter photon $E_{\mathrm{T}}^{\gamma}$ selection used to optimize the signal significance spin-1 $q\bar{q'}\to X^{\pm} \to W^{\pm}\gamma$. In addition to the total efficiency, contributions to the signal selection from each of the separate event categories are shown. The efficiencies calculated from MC samples with $W/Z$ hadronic decays are shown as the points on each curve. The line presents interpolated results.

Total efficiencies for the selection of signal events after categorization and application of the tighter photon $E_{\mathrm{T}}^{\gamma}$ selection used to optimize the signal significance spin-2 $q\bar{q}\to X^0 \to Z\gamma$. In addition to the total efficiency, contributions to the signal selection from each of the separate event categories are shown. The efficiencies calculated from MC samples with $W/Z$ hadronic decays are shown as the points on each curve. The line presents interpolated results.

Total efficiencies for the selection of signal events after categorization and application of the tighter photon $E_{\mathrm{T}}^{\gamma}$ selection used to optimize the signal significance spin-2 $gg\to X^0 \to Z\gamma$. In addition to the total efficiency, contributions to the signal selection from each of the separate event categories are shown. The efficiencies calculated from MC samples with $W/Z$ hadronic decays are shown as the points on each curve. The line presents interpolated results.

The $m_{J\gamma}$ distributions of data events selected for the spin-1 $q\bar{q'}\to X^{\pm} \to W^{\pm}\gamma$ search in the D2 category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-1 $q\bar{q'}\to X^{\pm} \to W^{\pm}\gamma$ search in the WMASS category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-0 $gg \to X^{0} \to Z\gamma$ search in the BTAG category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-0 $gg \to X^{0} \to Z\gamma$ search in the D2 category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-0 $gg \to X^{0} \to Z\gamma$ search in the ZMASS category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-2 $gg \to X^{0} \to Z\gamma$ search in the BTAG category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-2 $gg \to X^{0} \to Z\gamma$ search in the D2 category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-2 $gg \to X^{0} \to Z\gamma$ search in the ZMASS category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-2 $q\bar{q} \to X^{0} \to Z\gamma$ search in the BTAG category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-2 $q\bar{q} \to X^{0} \to Z\gamma$ search in the D2 category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The $m_{J\gamma}$ distributions of data events selected for the spin-2 $q\bar{q} \to X^{0} \to Z\gamma$ search in the ZMASS category. The background-only fit function shape is shown as the solid curve overlaid with a shaded band corresponding to statistical uncertainties in background parameters. Various signal shapes with cross-sections corresponding to expected limits obtained in this analysis are shown as dashed lines.

The 95% CL upper limits on $\sigma(pp\to X)\times B(X\to W/Z\gamma)$ as a function of $m_{X}$ for spin-0 $gg \to X^{0} \to Z\gamma$. The observed limits are shown as a solid black line and the expected ones are shown as a dashed line with the 1$\sigma$ (2$\sigma$) uncertainty band presented as the green (yellow) band.

The 95% CL upper limits on $\sigma(pp\to X)\times B(X\to W/Z\gamma)$ as a function of $m_{X}$ for spin-2 $gg \to X^{0} \to Z\gamma$. The observed limits are shown as a solid black line and the expected ones are shown as a dashed line with the 1$\sigma$ (2$\sigma$) uncertainty band presented as the green (yellow) band.

The 95% CL upper limits on $\sigma(pp\to X)\times B(X\to W/Z\gamma)$ as a function of $m_{X}$ for spin-2 $q\bar{q} \to X^{0} \to Z\gamma$. The observed limits are shown as a solid black line and the expected ones are shown as a dashed line with the 1$\sigma$ (2$\sigma$) uncertainty band presented as the green (yellow) band.

The 95% CL upper limits on $\sigma(pp\to X)\times B(X\to W/Z\gamma)$ as a function of $m_{X}$ for spin-1 $q\bar{q'}\to X^{\pm} \to W^{\pm}\gamma$. The observed limits are shown as a solid black line and the expected ones are shown as a dashed line with the 1$\sigma$ (2$\sigma$) uncertainty band presented as the green (yellow) band.

The jet mass distribution of large-$R$ jets originating from the hadronic decay of $W$ and $Z$ bosons produced from the decay of BSM bosons with mass $m_X = 2000$ GeV. The decays simulated are for the production models $q\bar{q'}\to X^{\pm} \to W^{\pm}\gamma$ with a spin-1 resonance $X^{\pm}$ and $gg\to X^0 \to Z\gamma$ with a spin-0 resonance $X^{0}$.

The photon $E_{T}$ selection criteria as a function of resonance mass for D2 categories. Selections for four different signal channels are shown together.

The photon $E_{T}$ selection criteria as a function of resonance mass for ZMASS or WMASS categories. Selections for four different signal channels are shown together.

Forward-rapidity $J/\psi$ —> $\mu^+\mu^-$ $d$+Au Nuclear Dependence at $\sqrt{s}$ = 200 GeV. (sys. A, B systematics are relative, i.e. they multiply the $R_{dAu}$ value. The sysA uncertainty includes statistical uncertainties as well as point-to-point uncorrelated systematics, sys. B represents uncertainties that are correlated from point to point, and sys. C represents uncertainties in the overall normalization.)

Mid rapidity $d$+Au —> $e^+e^-$ $J/\psi$ $R_{dAu}$ at $\sqrt{s}$=200 GeV. (Sys. A, B systematics are absolute, i.e. they add/subtract directly from $R_{dAu}$. The sys. A uncertainty includes statistical uncertainties as well as point-to-point uncorrelated systematics, sys. B represents uncertainties that are correlated from point to point, and sys. C represents uncertainties in the overall normalization.)

$d$+Au vs centrality vs $y$. (All uncertainties are absolute. The sys. A uncertainty includes both the statistical uncertainty and the point-to-point uncorrelated systematic, sys. B represents uncertainties that are correlated from point to point, and sys. C represents uncertainties in the overall normalization.)

Forward-rapidity $J/\psi$ —> $\mu^+\mu^-$ $d$+Au Nuclear Dependence at $\sqrt{s}$ = 200 GeV. (sys. A, B systematics are relative, i.e. they multiply the $R_{dAu}$ value. The sysA uncertainty includes statistical uncertainties as well as point-to-point uncorrelated systematics, sys. B represents uncertainties that are correlated from point to point, and sys. C represents uncertainties in the overall normalization.)

Mid rapidity $d$+Au —> $e^+e^-$ $J/\psi$ $R_{dAu}$ at $\sqrt{s}$=200 GeV. (Sys. A, B systematics are absolute, i.e. they add/subtract directly from $R_{dAu}$. The sys. A uncertainty includes statistical uncertainties as well as point-to-point uncorrelated systematics, sys. B represents uncertainties that are correlated from point to point, and sys. C represents uncertainties in the overall normalization.)

Forward-rapidity $J/\psi$ —> $\mu^+\mu^-$ $d$+Au Nuclear Dependence at $\sqrt{s}$ = 200 GeV. (sys. A, B systematics are relative, i.e. they multiply the $R_{CP}$ value. The sys. A uncertainty includes statistical uncertainties as well as point-to-point uncorrelated systematics, sys. B represents uncertainties that are correlated from point to point, and sys. C represents uncertainties in the overall normalization.)

Mid rapidity $d$+Au —> $e^+e^-$ $J/\psi$ $R_{CP}$ at $\sqrt{s}$=200 GeV. (Sys. A, B systematics are absolute, i.e. they add/subtract directly from $R_{CP}$. The sys. A uncertainty includes statistical uncertainties as well as point-to-point uncorrelated systematics, sys. B represents uncertainties that are correlated from point to point, and sys. C represents uncertainties in the overall normalization.)

Parametrization of the $A_{X}$ factor, defined as the fraction of diphoton resonances satisfying the fiducial acceptance at the particle level, as function of $m_{X}$ obtained from the narrow width simulated signal samples produced in gluon fusion.

The correction factor, $C_{X}$, defined as the ratio between the number of reconstructed signal events passing the analysis cuts and the number of signal events at the particle level generated within the fiducial volume, and acceptance correction factor, $A_{X}$, defined as the fraction of diphoton resonances satisfying the fiducial acceptance at the particle level. Both are computed for NWA spin-0 models as a function of $m_{X}$.

Effect of event selections on a scalar MC signal sample generated for $m_{X}$ = 15 GeV and on the data. For the MC sample, the efficiencies are shown after applying event weights and a truth level filter that requires two photons with $p^{\gamma\gamma}_{T}>40$ GeV; for the data, the absolute yields are shown. The initial yields for data include a trigger preselection that is the OR of a list of single photon and diphoton triggers. The "2 $loose$ photons" step includes the kinematic acceptance cuts.

Parameterization of the Double Sided Crystal Ball function parameters describing the scalar mass resolution model as a function of $m_{X}$ [GeV].

The reconstruction efficiency for caloDPJs produced by the decay of &gamma;_d into e⁺e^- or qq&#772;. The reconstruction efficiency is shown for &gamma;_d with 0&lt;|&eta;|&lt;1.1 as a function of their transverse momentum in events where the &gamma;_d L_xy is between 2&nbsp;m and 4&nbsp;m.

Observed 95% CL upper limits on the branching ratio (B) for the decay H&rarr;&nbsp;2&gamma;_d+X and ggF selection, for different &gamma;_d masses and a 125&nbsp;GeV Higgs boson. The limits are shown for the SR_2&mu;^ggF, assuming an FRVZ signal model. The hatched band denotes the region in which the branching ratio of H&rarr;&nbsp;2&gamma;_d+X is larger than unity.

Observed 95% CL upper limits on the branching ratio (B) for the decay H&rarr;&nbsp;2&gamma;_d+X and ggF selection, for different &gamma;_d masses and a 125&nbsp;GeV Higgs boson. The limits are shown for the SR_c+&mu;^ggF, assuming an FRVZ signal model. The hatched band denotes the region in which the branching ratio of H&rarr;&nbsp;2&gamma;_d+X is larger than unity.

Observed 95% CL upper limits on the branching ratio (B) for the decay H&rarr;&nbsp;2&gamma;_d+X and ggF selection, for different &gamma;_d masses and a 125&nbsp;GeV Higgs boson. The limits are shown for the SR_2c^ggF, assuming an FRVZ signal model. The hatched band denotes the region in which the branching ratio of H&rarr;&nbsp;2&gamma;_d+X is larger than unity.

Observed 95% CL upper limits on the branching ratio (B) for the decay H&rarr;&nbsp;2&gamma;_d+X and WH selection, for different &gamma;_d masses and a 125&nbsp;GeV Higgs boson. The limits are shown for the SR_c^WH, assuming an FRVZ signal model. The hatched band denotes the region in which the branching ratio of H&rarr;&nbsp;2&gamma;_d+X is larger than unity.

Observed 95% CL upper limits on the branching ratio (B) for the decay H&rarr;&nbsp;2&gamma;_d+X and WH selection, for different &gamma;_d masses and a 125&nbsp;GeV Higgs boson. The limits are shown for the SR_c+&mu;^WH, assuming an FRVZ signal model. The hatched band denotes the region in which the branching ratio of H&rarr;&nbsp;2&gamma;_d+X is larger than unity.

Observed 95% CL upper limits on the branching ratio (B) for the decay H&rarr;&nbsp;2&gamma;_d+X and WH selection, for different &gamma;_d masses and a 125&nbsp;GeV Higgs boson. The limits are shown for the SR_2c^WH, assuming an FRVZ signal model. The hatched band denotes the region in which the branching ratio of H&rarr;&nbsp;2&gamma;_d+X is larger than unity.

Observed 95% CL upper limits on the branching ratio (B) for the decay H&rarr;&nbsp;2&gamma;_d and ggF selection, for different &gamma;_d masses and a 125&nbsp;GeV Higgs boson. The limits are shown for the SR_2&mu;^ggF, assuming a HAHM signal model. The hatched band denotes the region in which the branching ratio of H&rarr;&nbsp;2&gamma;_d is larger than unity.

Observed 95% CL upper limits on the branching ratio (B) for the decay H&rarr;&nbsp;2&gamma;_d and ggF selection, for different &gamma;_d masses and a 125&nbsp;GeV Higgs boson. The limits are shown for the SR_c+&mu;^ggF, assuming a HAHM signal model. The hatched band denotes the region in which the branching ratio of H&rarr;&nbsp;2&gamma;_d is larger than unity.

Observed 95% CL upper limits on the branching ratio (B) for the decay H&rarr;&nbsp;2&gamma;_d and ggF selection, for different &gamma;_d masses and a 125&nbsp;GeV Higgs boson. The limits are shown for the SR_2c^ggF, assuming a HAHM signal model. The hatched band denotes the region in which the branching ratio of H&rarr;&nbsp;2&gamma;_d is larger than unity.

The CalRatio 2015--2016 trigger efficiency for the decay of &gamma;_d in e⁺e^- or qq&#772;. The trigger efficiency is shown for &gamma;_d with 0&lt;|&eta;|&lt;1.1 as function of the transverse decay length L_xy.

The CalRatio 2015--2016 trigger efficiency for the decay of &gamma;_d in e⁺e^- or qq&#772;. The trigger efficiency is shown for &gamma;_d with 0&lt;|&eta;|&lt;1.1 as function of the transverse momentum, in events where the &gamma;_d L_xy is between 2&nbsp;m and 4&nbsp;m.

The CalRatio 2017--2018 trigger efficiency for the decay of &gamma;_d in e⁺e^- or qq&#772;. The trigger efficiency is shown for &gamma;_d with 0&lt;|&eta;|&lt;1.1 as function of the transverse decay length L_xy.

The CalRatio 2017--2018 trigger efficiency for the decay of &gamma;_d in e⁺e^- or qq&#772;. The trigger efficiency is shown for &gamma;_d with 0&lt;|&eta;|&lt;1.1 as function of the transverse momentum, in events where the &gamma;_d L_xy is between 2&nbsp;m and 4&nbsp;m.

The narrow-scan 2015--2016 trigger efficiency for the decay of &gamma;_d in &mu;⁺&mu;^-. The trigger efficiency is shown for &gamma;_d with 0&lt;|&eta;|&lt;1.1 as function of the transverse decay length L_xy.

The narrow-scan 2015--2016 trigger efficiency for the decay of &gamma;_d in &mu;⁺&mu;^-. The trigger efficiency is shown for &gamma;_d with 0&lt;|&eta;|&lt;1.1 as function of the transverse momentum, in events where the &gamma;_d L_xy is below 6&nbsp;m.

The narrow-scan 2017--2018 trigger efficiency for the decay of &gamma;_d in &mu;⁺&mu;^-. The trigger efficiency is shown for &gamma;_d with 0&lt;|&eta;|&lt;1.1 as function of the transverse decay length L_xy.

The narrow-scan 2017--2018 trigger efficiency for the decay of &gamma;_d in &mu;⁺&mu;^-. The trigger efficiency is shown for &gamma;_d with 0&lt;|&eta;|&lt;1.1 as function of the transverse momentum, in events where the &gamma;_d L_xy is below 6&nbsp;m.

Efficiency of the cosmic-ray tagger as function of the &gamma;_d transverse decay length. The efficiency is calculated accepting the &mu;DPJs for which the cosmic-ray tagger score is &gt; 0.2 for each associated MS-only track.

Efficiency of the cosmic-ray tagger as function of the &gamma;_d transverse momentum. The efficiency is calculated accepting the &mu;DPJs for which the cosmic-ray tagger score is &gt; 0.2 for each associated MS-only track.

Efficiency of the QCD tagger as function of the &gamma;_d transverse decay length. The efficiency is calculated accepting the caloDPJs for which the QCD tagger score is &gt; 0.5.

Efficiency of the QCD tagger as function of the &gamma;_d transverse momentum. The efficiency is calculated accepting the caloDPJs for which the QCD tagger score is &gt; 0.5.

The extrapolated acceptance times efficiencies as a function of the &gamma;_d mean proper lifetime c&tau; for the FRVZ pp &rarr; H&rarr;&nbsp;2&gamma;_d+X process and a SM Higgs boson for the SR_2&mu;^ggF.

The extrapolated acceptance times efficiencies as a function of the &gamma;_d mean proper lifetime c&tau; for the FRVZ pp &rarr; H&rarr;&nbsp;2&gamma;_d+X process and a SM Higgs boson for the SR_c+&mu;^ggF.

The extrapolated acceptance times efficiencies as a function of the &gamma;_d mean proper lifetime c&tau; for the FRVZ pp &rarr; H&rarr;&nbsp;2&gamma;_d+X process and a SM Higgs boson for the SR_2c^ggF.

The extrapolated acceptance times efficiencies as a function of the &gamma;_d mean proper lifetime c&tau; for the FRVZ pp &rarr; H&rarr;&nbsp;2&gamma;_d+X process and a SM Higgs boson for the SR_c^WH.

The extrapolated acceptance times efficiencies as a function of the &gamma;_d mean proper lifetime c&tau; for the FRVZ pp &rarr; H&rarr;&nbsp;2&gamma;_d+X process and a SM Higgs boson for the SR_2c^WH.

The extrapolated acceptance times efficiencies as a function of the &gamma;_d mean proper lifetime c&tau; for the FRVZ pp &rarr; H&rarr;&nbsp;2&gamma;_d+X process and a SM Higgs boson for the SR_c+&mu;^WH.

The extrapolated acceptance times efficiencies for SR_2&mu;^ggF, as a function of the &gamma;_d mean proper lifetime c&tau; for the FRVZ pp &rarr; H&rarr;&nbsp;2&gamma;_d+X process and a Higgs-like scalar with a mass of 800&nbsp;GeV. The coloured bands represent the MC statistical uncertainties.

The extrapolated acceptance times efficiencies for SR_c+&mu;^ggF, as a function of the &gamma;_d mean proper lifetime c&tau; for the FRVZ pp &rarr; H&rarr;&nbsp;2&gamma;_d+X process and a Higgs-like scalar with a mass of 800&nbsp;GeV. The coloured bands represent the MC statistical uncertainties.

The extrapolated acceptance times efficiencies for SR_2c;^ggF, as a function of the &gamma;_d mean proper lifetime c&tau; for the FRVZ pp &rarr; H&rarr;&nbsp;2&gamma;_d+X process and a Higgs-like scalar with a mass of 800&nbsp;GeV. The coloured bands represent the MC statistical uncertainties.

The extrapolated acceptance times efficiencies for SR_2&mu;^ggF, as a function of the &gamma;_d mean proper lifetime c&tau; for the HAHM H&rarr;&nbsp;2&gamma;_d+X process and a SM Higgs boson for the ggF channel.

The extrapolated acceptance times efficiencies for SR_c+&mu;^ggF, as a function of the &gamma;_d mean proper lifetime c&tau; for the HAHM H&rarr;&nbsp;2&gamma;_d+X process and a SM Higgs boson for the ggF channel.

The extrapolated acceptance times efficiencies for SR_2c;^ggF, as a function of the &gamma;_d mean proper lifetime c&tau; for the HAHM H&rarr;&nbsp;2&gamma;_d+X process and a SM Higgs boson for the ggF channel.

The extrapolated acceptance times efficiencies for SR_2&mu;^ggF, as a function of the &gamma;_d mean proper lifetime c&tau; for the FRVZ pp &rarr; H&rarr;&nbsp;4&gamma;_d+X process and a Higgs-like scalar with a mass of 800&nbsp;GeV. The coloured bands represent the MC statistical uncertainties.

The extrapolated acceptance times efficiencies for SR_c+&mu;^ggF, as a function of the &gamma;_d mean proper lifetime c&tau; for the FRVZ pp &rarr; H&rarr;&nbsp;4&gamma;_d+X process and a Higgs-like scalar with a mass of 800&nbsp;GeV. The coloured bands represent the MC statistical uncertainties.

The extrapolated acceptance times efficiencies for SR_2c;^ggF, as a function of the &gamma;_d mean proper lifetime c&tau; for the FRVZ pp &rarr; H&rarr;&nbsp;4&gamma;_d+X process and a Higgs-like scalar with a mass of 800&nbsp;GeV. The coloured bands represent the MC statistical uncertainties.

The extrapolated acceptance times efficiencies for SR_2&mu;^ggF, as a function of the &gamma;_d mean proper lifetime c&tau; for the FRVZ pp &rarr; H&rarr;&nbsp;4&gamma;_d+X process and a SM Higgs boson. The coloured bands represent the MC statistical uncertainties.

The extrapolated acceptance times efficiencies for SR_c+&mu;^ggF, as a function of the &gamma;_d mean proper lifetime c&tau; for the FRVZ pp &rarr; H&rarr;&nbsp;4&gamma;_d+X process and a SM Higgs boson. The coloured bands represent the MC statistical uncertainties.

The extrapolated acceptance times efficiencies for SR_2c;^ggF, as a function of the &gamma;_d mean proper lifetime c&tau; for the FRVZ pp &rarr; H&rarr;&nbsp;4&gamma;_d+X process and a SM Higgs boson. The coloured bands represent the MC statistical uncertainties.

Observed limits at the 95% CL on the branching ratio (B), obtained from the SR_2&mu;^ggF, for the process pp &rarr; H&rarr;&nbsp;4&gamma;_d+X, for different &gamma;_d masses and a 125 GeV Higgs boson. The limits are shown assuming an FRVZ signal model.

Observed limits at the 95% CL on the branching ratio (B), obtained from the SR_c+&mu;^ggF, for the process pp &rarr; H&rarr;&nbsp;4&gamma;_d+X, for different &gamma;_d masses and a 125 GeV Higgs boson. The limits are shown assuming an FRVZ signal model.

Observed limits at the 95% CL on the branching ratio (B), obtained from the SR_2c^ggF, for the process pp &rarr; H&rarr;&nbsp;4&gamma;_d+X, for different &gamma;_d masses and a 125 GeV Higgs boson. The limits are shown assuming an FRVZ signal model.

Observed limits at the 95% CL on the cross section, obtained from the SR_2&mu;^ggF, for the process pp &rarr; H&rarr;&nbsp;2&gamma;_d+X, for different &gamma;_d masses and a Higgs-like scalar with a mass of 800&nbsp;GeV. The limits are shown assuming an FRVZ signal model.

Observed limits at the 95% CL on the cross section, obtained from the SR_c+&mu;^ggF, for the process pp &rarr; H&rarr;&nbsp;2&gamma;_d+X, for different &gamma;_d masses and a Higgs-like scalar with a mass of 800&nbsp;GeV. The limits are shown assuming an FRVZ signal model.

Observed limits at the 95% CL on the cross section, obtained from the SR_2c^ggF, for the process pp &rarr; H&rarr;&nbsp;2&gamma;_d+X and ggF selection, for different &gamma;_d masses and a Higgs-like scalar with a mass of 800&nbsp;GeV. The limits are shown assuming an FRVZ signal model.

Observed limits at the 95% CL on the cross section for the process pp &rarr; H&rarr;&nbsp;4&gamma;_d+X and ggF selection, for different &gamma;_d masses and a Higgs-like scalar with a mass of 800&nbsp;GeV. The limits are shown for the 2&mu; search channel, assuming an FRVZ signal model.

Observed limits at the 95% CL on the cross section for the process pp &rarr; H&rarr;&nbsp;4&gamma;_d+X and ggF selection, for different &gamma;_d masses and a Higgs-like scalar with a mass of 800&nbsp;GeV. The limits are shown for the c+&mu; search channel, assuming an FRVZ signal model.

Observed limits at the 95% CL on the cross section for the process pp &rarr; H&rarr;&nbsp;4&gamma;_d+X and ggF selection, for different &gamma;_d masses and a Higgs-like scalar with a mass of 800&nbsp;GeV. The limits are shown for the 2c search channel, assuming an FRVZ signal model.

Comparison between data and background prediction before the fit (Pre-Fit) for the mass of the FCNC top-quark candidate in SR1. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The four FCNC LH signals are also shown separately, normalized to five times the cross-section corresponding to the most stringent observed branching ratio limits. The first (last) bin in all distributions includes the underflow (overflow). The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction before the fit (Pre-Fit) for the mass of the SM top-quark candidate in SR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The four FCNC LH signals are also shown separately, normalized to five times the cross-section corresponding to the most stringent observed branching ratio limits. The first (last) bin in all distributions includes the underflow (overflow). The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction before the fit (Pre-Fit) for the transverse momentum of the Z boson candidate in SR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The four FCNC LH signals are also shown separately, normalized to five times the cross-section corresponding to the most stringent observed branching ratio limits. The first (last) bin in all distributions includes the underflow (overflow). The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the $D_{1}$ discriminant in the mass sideband CR1. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also separately shown, normalized to 500 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the $D_{2}^{u}$ discriminant in the mass sideband CR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also separately shown, normalized to 500 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the $D_{1}$ discriminant in SR1. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also separately shown, normalized to 50 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the $D_{2}^{u}$ discriminant in SR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also separately shown, normalized to 50 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZc LH coupling extraction. The distribution is for the $D_{1}$ discriminant in the mass sideband CR1. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZc LH signals are also separately shown, normalized to 500 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZc LH coupling extraction. The distribution is for the $D_{2}^{c}$ discriminant in the mass sideband CR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZc LH signals are also separately shown, normalized to 500 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZc LH coupling extraction. The distribution is for the $D_{1}$ discriminant in SR1. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZc LH signals are also separately shown, normalized to 50 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZc LH coupling extraction. The distribution is for the $D_{2}^{c}$ discriminant in SR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZc LH signals are also separately shown, normalized to 50 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the leading lepton $p_{T}$ in the $t\bar{t}$ CR. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also shown separately, normalized to $10^{3}$ times the best fit of the signal yield. The last bin includes the overflow. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the third lepton $p_{T}$ in the $t\bar{t}$ CR. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also shown separately, normalized to $10^{3}$ times the best fit of the signal yield. The last bin includes the overflow. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the leading lepton $p_{T}$ in the $t\bar{t}Z$ CR. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also shown separately, normalized to $10^{3}$ times the best fit of the signal yield. The last bin includes the overflow. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the $D_{1}$ discriminant in the $t\bar{t}Z$ CR. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also shown separately, normalized to $10^{3}$ times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).

Measured differential cross section as a function of observable $ p_{T}^{ll} - p_{T}^{\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll} + p_{T}^{\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll} + p_{T}^{\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ \Delta R (l,l)$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ \Delta R (l,l)$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ N_{jets}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ N_{jets}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{Jet1}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{Jet1}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{Jet2}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{Jet2}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{Jet2}/p_{T}^{Jet1}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{Jet2}/p_{T}^{Jet1}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ m_{jj}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ m_{jj}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ m_{ll\gamma j}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ m_{ll\gamma j}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ H_{T}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ H_{T}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{\gamma} / \sqrt{H_{T}}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{\gamma} / \sqrt{H_{T}}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ \Delta \phi (Jet,\gamma)$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ \Delta \phi (Jet,\gamma)$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma j}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma j}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ \phi_{CS}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ \phi_{CS}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ \cos \theta_{CS}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ \cos \theta_{CS}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma} / m_{ll\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma} / m_{ll\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma} / m_{ll\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma} / m_{ll\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma} / m_{ll\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma} / m_{ll\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll} - p_{T}^{\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll} - p_{T}^{\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll} - p_{T}^{\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll} - p_{T}^{\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll} - p_{T}^{\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll} - p_{T}^{\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma j}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma j}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma j}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma j}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma j}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma j}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{\gamma} / \sqrt{H_{T}}$ (Fig. 8 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{\gamma} / \sqrt{H_{T}}$ (Fig. 8 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ H_{T}$ (Fig. 8 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ H_{T}$ (Fig. 8 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ \Delta \phi (Jet,\gamma)$ (Fig. 8 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ \Delta \phi (Jet,\gamma)$ (Fig. 8 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ \Delta R (l,l)$ (Fig. 5 (d))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ \Delta R (l,l)$ (Fig. 5 (d))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll} - p_{T}^{\gamma}$ (Fig. 5 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll} - p_{T}^{\gamma}$ (Fig. 5 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll} - p_{T}^{\gamma} \textrm{ in bin } p_{T}^{ll} + p_{T}^{\gamma} < 200 GeV$ (Fig. 11 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll} - p_{T}^{\gamma} \textrm{ in bin } p_{T}^{ll} + p_{T}^{\gamma} < 200 GeV$ (Fig. 11 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll} - p_{T}^{\gamma} \textrm{ in bin } 200 GeV < p_{T}^{ll} + p_{T}^{\gamma} < 300 GeV$ (Fig. 11 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll} - p_{T}^{\gamma} \textrm{ in bin } 200 GeV < p_{T}^{ll} + p_{T}^{\gamma} < 300 GeV$ (Fig. 11 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll} - p_{T}^{\gamma} \textrm{ in bin } p_{T}^{ll} + p_{T}^{\gamma} > 300 GeV$ (Fig. 11 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll} - p_{T}^{\gamma} \textrm{ in bin } p_{T}^{ll} + p_{T}^{\gamma} > 300 GeV$ (Fig. 11 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ m_{jj}$ (Fig. 7 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ m_{jj}$ (Fig. 7 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ m_{ll\gamma j}$ (Fig. 7 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ m_{ll\gamma j}$ (Fig. 7 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ N_{jets}$ (Fig. 6 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ N_{jets}$ (Fig. 6 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{Jet1}$ (Fig. 6 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{Jet1}$ (Fig. 6 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{Jet2}$ (Fig. 6 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{Jet2}$ (Fig. 6 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{Jet2}/p_{T}^{Jet1}$ (Fig. 6 (d))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{Jet2}/p_{T}^{Jet1}$ (Fig. 6 (d))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll}$ (Fig. 5 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll}$ (Fig. 5 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma j}$ (Fig. 8 (d))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma j}$ (Fig. 8 (d))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma j} \textrm{ in bin } p_{T}^{ll\gamma} < 50 GeV$ (Fig. 12 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma j} \textrm{ in bin } p_{T}^{ll\gamma} < 50 GeV$ (Fig. 12 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma j} \textrm{ in bin } 50 GeV < p_{T}^{ll\gamma} < 75 GeV$ (Fig. 12 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma j} \textrm{ in bin } 50 GeV < p_{T}^{ll\gamma} < 75 GeV$ (Fig. 12 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma j} \textrm{ in bin } p_{T}^{ll\gamma} > 75 GeV$ (Fig. 12 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma j} \textrm{ in bin } p_{T}^{ll\gamma} > 75 GeV$ (Fig. 12 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma} / m_{ll\gamma} \textrm{ in bin } 125 GeV < m_{ll\gamma} < 200 GeV$ (Fig. 10 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma} / m_{ll\gamma} \textrm{ in bin } 125 GeV < m_{ll\gamma} < 200 GeV$ (Fig. 10 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma} / m_{ll\gamma} \textrm{ in bin } 200 GeV < m_{ll\gamma} < 300 GeV$ (Fig. 10 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma} / m_{ll\gamma} \textrm{ in bin } 200 GeV < m_{ll\gamma} < 300 GeV$ (Fig. 10 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma} / m_{ll\gamma} \textrm{ in bin } m_{ll\gamma} > 300 GeV$ (Fig. 10 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma} / m_{ll\gamma} \textrm{ in bin } m_{ll\gamma} > 300 GeV$ (Fig. 10 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll} + p_{T}^{\gamma}$ (Fig. 5 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll} + p_{T}^{\gamma}$ (Fig. 5 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ \cos \theta_{CS}$ (Fig. 9 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ \cos \theta_{CS}$ (Fig. 9 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ \phi_{CS}$ (Fig. 9 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ \phi_{CS}$ (Fig. 9 (a))

The observed and expected 95% CL upper limits on the minimal coupling vector LQ pair production cross-sections assuming B = 1 as a function of m$_\text{LQ}$.

The observed and expected 95% CL upper limits on the Yang-Mills coupling vector LQ pair production cross-sections assuming B = 1 as a function of m$_\text{LQ}$.

The observed and expected 95% CL upper limits on the branching ratio into charged leptons as a function of m$_\text{LQ}$ for the scalar LQ.

The observed and expected 95% CL upper limits on the branching ratio into charged leptons as a function of m$_\text{LQ}$ for the vector LQ minimal coupling scenario.

The observed and expected 95% CL upper limits on the branching ratio into charged leptons as a function of m$_\text{LQ}$ for the vector LQ Yang-Mills coupling scenario.

Breakdown of systematic uncertainties in the measured inclusive cross-section. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper.

Data bootstrap post unfolding for the inclusive cross-section. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb].

Differential absolute cross-section for $\textrm{p}_{\textrm{T}}^{l}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The last bin of the distribution contains the overflow.

Covariance Matrix (statistical uncertainties only) for the differential absolute cross-section for $\textrm{p}_{\textrm{T}}^{l}$. The last bin of the distribution contains the overflow.

Data bootstrap post unfolding for the differential absolute cross-section for $\textrm{p}_{\textrm{T}}^{l}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV]. The last bin of the distribution contains the overflow.

Differential absolute cross-section for $|\eta^{l}|$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper.

Covariance Matrix (statistical uncertainties only) for the differential absolute cross-section for $|\eta^{l}|$.

Data bootstrap post unfolding for the differential absolute cross-section for $|\eta^{l}|$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb].

Differential absolute cross-section for $\textrm{E}^{e} + \textrm{E}^{\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The last bin of the distribution contains the overflow.

Covariance Matrix (statistical uncertainties only) for the differential absolute cross-section for $\textrm{E}^{e} + \textrm{E}^{\mu}$. The last bin of the distribution contains the overflow.

Data bootstrap post unfolding for the differential absolute cross-section for $\textrm{E}^{e} + \textrm{E}^{\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV]. The last bin of the distribution contains the overflow.

Differential absolute cross-section for $m^{e\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The last bin of the distribution contains the overflow.

Covariance Matrix (statistical uncertainties only) for the differential absolute cross-section for $m^{e\mu}$. The last bin of the distribution contains the overflow.

Data bootstrap post unfolding for the differential absolute cross-section for $m^{e\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV]. The last bin of the distribution contains the overflow.

Differential absolute cross-section for $\textrm{p}_{\textrm{T}}^{e} + \textrm{p}_{\textrm{T}}^{\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The last bin of the distribution contains the overflow.

Covariance Matrix (statistical uncertainties only) for the differential absolute cross-section for $\textrm{p}_{\textrm{T}}^{e} + \textrm{p}_{\textrm{T}}^{\mu}$. The last bin of the distribution contains the overflow.

Data bootstrap post unfolding for the differential absolute cross-section for $\textrm{p}_{\textrm{T}}^{e} + \textrm{p}_{\textrm{T}}^{\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV]. The last bin of the distribution contains the overflow.

Differential absolute cross-section for $\textrm{p}_{\textrm{T}}^{e\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The last bin of the distribution contains the overflow.

Covariance Matrix (statistical uncertainties only) for the differential absolute cross-section for $\textrm{p}_{\textrm{T}}^{e\mu}$. The last bin of the distribution contains the overflow.

Data bootstrap post unfolding for the differential absolute cross-section for $\textrm{p}_{\textrm{T}}^{e\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV]. The last bin of the distribution contains the overflow.

Differential absolute cross-section for $|\Delta\phi^{e\mu}|$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper.

Covariance Matrix (statistical uncertainties only) for the differential absolute cross-section for $|\Delta\phi^{e\mu}|$.

Data bootstrap post unfolding for the differential absolute cross-section for $|\Delta\phi^{e\mu}|$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/rad].

Differential absolute cross-section for $|y^{e\mu}|$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper.

Covariance Matrix (statistical uncertainties only) for the differential absolute cross-section for $|y^{e\mu}|$.

Data bootstrap post unfolding for the differential absolute cross-section for $|y^{e\mu}|$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb].

Double differential absolute cross-section for $|y^{e\mu}|$ x $m^{e\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The highest invariant mass bin of the two distributions contains the overflows.

Covariance Matrix (statistical uncertainties only) for the double differential absolute cross-section for $|y^{e\mu}|$ x $m^{e\mu}$. The highest invariant mass bin of the two distributions contains the overflows.

Data bootstrap post unfolding for the double differential absolute cross-section for $|y^{e\mu}|$ x $m^{e\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV]. The highest invariant mass bin of the two distributions contains the overflows.

Double differential absolute cross-section for $|\Delta\phi^{e\mu}|$ x $m^{e\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The highest invariant mass bin of the two distributions contains the overflows.

Covariance Matrix (statistical uncertainties only) for the double differential absolute cross-section for $|\Delta\phi^{e\mu}|$ x $m^{e\mu}$. The highest invariant mass bin of the two distributions contains the overflows.

Data bootstrap post unfolding for the double differential absolute cross-section for $|\Delta\phi^{e\mu}|$ x $m^{e\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV/rad]. The highest invariant mass bin of the two distributions contains the overflows.

Double differential absolute cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{p}_{\textrm{T}}^{e\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The highest $\textrm{p}_{\textrm{T}}^{e\mu}$ bin contains the overflows.

Covariance Matrix (statistical uncertainties only) for the double differential absolute cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{p}_{\textrm{T}}^{e\mu}$. The highest $\textrm{p}_{\textrm{T}}^{e\mu}$ bin contains the overflows.

Data bootstrap post unfolding for the double differential absolute cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{p}_{\textrm{T}}^{e\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV/rad]. The highest $\textrm{p}_{\textrm{T}}^{e\mu}$ bin contains the overflows.

Double differential absolute cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{E}^{e} + \textrm{E}^{\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The highest $\textrm{E}^{e} + \textrm{E}^{\mu}$ bin contains the overflows.

Covariance Matrix (statistical uncertainties only) for the double differential absolute cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{E}^{e} + \textrm{E}^{\mu}$. The highest $\textrm{E}^{e} + \textrm{E}^{\mu}$ bin contains the overflows.

Data bootstrap post unfolding for the double differential absolute cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{E}^{e} + \textrm{E}^{\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [fb/GeV/rad]. The highest $\textrm{E}^{e} + \textrm{E}^{\mu}$ bin contains the overflows.

Differential normalised cross-section for $\textrm{p}_{\textrm{T}}^{l}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The last bin of the distribution contains the overflow.

Covariance Matrix (statistical uncertainties only) for the differential normalised cross-section for $\textrm{p}_{\textrm{T}}^{l}$. The last bin of the distribution contains the overflow.

Data bootstrap post unfolding for the differential normalised cross-section for $\textrm{p}_{\textrm{T}}^{l}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/GeV]. The last bin of the distribution contains the overflow.

Differential normalised cross-section for $|\eta^{l}|$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper.

Covariance Matrix (statistical uncertainties only) for the differential normalised cross-section for $|\eta^{l}|$.

Data bootstrap post unfolding for the differential normalised cross-section for $|\eta^{l}|$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry.

Differential normalised cross-section for $\textrm{E}^{e} + \textrm{E}^{\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The last bin of the distribution contains the overflow.

Covariance Matrix (statistical uncertainties only) for the differential normalised cross-section for $\textrm{E}^{e} + \textrm{E}^{\mu}$. The last bin of the distribution contains the overflow.

Data bootstrap post unfolding for the differential normalised cross-section for $\textrm{E}^{e} + \textrm{E}^{\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry. The last bin of the distribution contains the overflow.

Differential normalised cross-section for $m^{e\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The last bin of the distribution contains the overflow.

Covariance Matrix (statistical uncertainties only) for the differential normalised cross-section for $m^{e\mu}$. The last bin of the distribution contains the overflow.

Data bootstrap post unfolding for the differential normalised cross-section for $m^{e\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry. The last bin of the distribution contains the overflow.

Differential normalised cross-section for $\textrm{p}_{\textrm{T}}^{e} + \textrm{p}_{\textrm{T}}^{\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The last bin of the distribution contains the overflow.

Covariance Matrix (statistical uncertainties only) for the differential normalised cross-section for $\textrm{p}_{\textrm{T}}^{e} + \textrm{p}_{\textrm{T}}^{\mu}$. The last bin of the distribution contains the overflow.

Data bootstrap post unfolding for the differential normalised cross-section for $\textrm{p}_{\textrm{T}}^{e} + \textrm{p}_{\textrm{T}}^{\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/GeV]. The last bin of the distribution contains the overflow.

Differential normalised cross-section for $\textrm{p}_{\textrm{T}}^{e\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The last bin of the distribution contains the overflow.

Covariance Matrix (statistical uncertainties only) for the differential normalised cross-section for $\textrm{p}_{\textrm{T}}^{e\mu}$. The last bin of the distribution contains the overflow.

Data bootstrap post unfolding for the differential normalised cross-section for $\textrm{p}_{\textrm{T}}^{e\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/GeV]. The last bin of the distribution contains the overflow.

Differential normalised cross-section for $|\Delta\phi^{e\mu}|$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper.

Covariance Matrix (statistical uncertainties only) for the differential normalised cross-section for $|\Delta\phi^{e\mu}|$.

Data bootstrap post unfolding for the differential normalised cross-section for $|\Delta\phi^{e\mu}|$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/rad].

Differential normalised cross-section for $|y^{e\mu}|$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper.

Covariance Matrix (statistical uncertainties only) for the differential normalised cross-section for $|y^{e\mu}|$.

Data bootstrap post unfolding for the differential normalised cross-section for $|y^{e\mu}|$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry.

Double differential normalised cross-section for $|y^{e\mu}|$ x $m^{e\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The highest invariant mass bin of the two distributions contains the overflows.

Covariance Matrix (statistical uncertainties only) for the double differential normalised cross-section for $|y^{e\mu}|$ x $m^{e\mu}$. The highest invariant mass bin of the two distributions contains the overflows.

Data bootstrap post unfolding for the double differential normalised cross-section for $|y^{e\mu}|$ x $m^{e\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/GeV]. The highest invariant mass bin of the two distributions contains the overflows.

Double differential normalised cross-section for $|\Delta\phi^{e\mu}|$ x $m^{e\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The highest invariant mass bin of the two distributions contains the overflows.

Covariance Matrix (statistical uncertainties only) for the double differential normalised cross-section for $|\Delta\phi^{e\mu}|$ x $m^{e\mu}$. The highest invariant mass bin of the two distributions contains the overflows.

Data bootstrap post unfolding for the double differential normalised cross-section for $|\Delta\phi^{e\mu}|$ x $m^{e\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/GeV/rad]. The highest invariant mass bin of the two distributions contains the overflows.

Double differential normalised cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{p}_{\textrm{T}}^{e\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The highest $\textrm{p}_{\textrm{T}}^{e\mu}$ bin contains the overflows.

Covariance Matrix (statistical uncertainties only) for the double differential normalised cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{p}_{\textrm{T}}^{e\mu}$. The highest $\textrm{p}_{\textrm{T}}^{e\mu}$ bin contains the overflows.

Data bootstrap post unfolding for the double differential normalised cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{p}_{\textrm{T}}^{e\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/GeV/rad]. The highest $\textrm{p}_{\textrm{T}}^{e\mu}$ bin contains the overflows.

Double differential normalised cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{E}^{e} + \textrm{E}^{\mu}$. The impact of the top-quark mass on the cross-section is included in the table and not counted in the total uncertainty entry in the paper. The highest $\textrm{E}^{e} + \textrm{E}^{\mu}$ bin contains the overflows.

Covariance Matrix (statistical uncertainties only) for the double differential normalised cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{E}^{e} + \textrm{E}^{\mu}$. The highest $\textrm{E}^{e} + \textrm{E}^{\mu}$ bin contains the overflows.

Data bootstrap post unfolding for the double differential normalised cross-section for $|\Delta\phi^{e\mu}|$ x $\textrm{E}^{e} + \textrm{E}^{\mu}$. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the BootstrapGenerator software package (https://gitlab.cern.ch/atlas-physics/sm/StandardModelTools_BootstrapGenerator/BootstrapGenerator), which implements a technique described in ATL-PHYS-PUB-2021-011 (https://cds.cern.ch/record/2759945). The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. All the provided numbers originate from pseudo-data, including the 0th entry, and are in units of [1/GeV/rad]. The highest $\textrm{E}^{e} + \textrm{E}^{\mu}$ bin contains the overflows.

Signal region detector-level distribution for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$.

Signal region detector-level distribution for the observable $m_{e\mu}$.

Signal region detector-level distribution for the observable $p_{\mathrm{T}}^{e\mu}$.

Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|y_{e\mu}|$

Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|y_{e\mu}|$

Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|\Delta \phi(e \mu)|$

Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|\Delta \phi(e \mu)|$

Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $ \cos\theta^{\ast}$

Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $ \cos\theta^{\ast}$

Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$

Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$

Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $m_{e\mu}$

Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $m_{e\mu}$

Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{e\mu}$

Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{e\mu}$

The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|y_{e\mu}|$

The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|y_{e\mu}|$

The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|\Delta \phi(e \mu)|$

The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|\Delta \phi(e \mu)|$

The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $ \cos\theta^{\ast}$

The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $ \cos\theta^{\ast}$

The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$

The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$

The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $m_{e\mu}$

The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $m_{e\mu}$

The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{e\mu}$

The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{e\mu}$

Probability of finding at least one TAR jet, where the p_T-leading TAR jet passes the m_Wcand and D₂^&beta;=1 requirements, as a function of m_s. The probability is determined in a sample of signal events with m_Z'=2100 GeV, with the preselections applied.

Probability of finding a W_had candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m_s. The probability is determined in a sample of signal events with m_Z'=500 GeV, with the preselections applied that do not pass the requirements of the merged category.

Probability of finding a W_had candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m_s. The probability is determined in a sample of signal events with m_Z'=1000 GeV, with the preselections applied that do not pass the requirements of the merged category.

Probability of finding a W_had candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m_s. The probability is determined in a sample of signal events with m_Z'=1700 GeV, with the preselections applied that do not pass the requirements of the merged category.

Probability of finding a W_had candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m_s. The probability is determined in a sample of signal events with m_Z'=2100 GeV, with the preselections applied that do not pass the requirements of the merged category.

Observed exclusion contour at 95&#37; C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_&chi;=1, m_&chi;=200 GeV, and sin&theta;=0.01.

Expected exclusion contour at 95&#37; C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_&chi;=1, m_&chi;=200 GeV, and sin&theta;=0.01.

Expected+1&sigma; exclusion contour at 95&#37; C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_&chi;=1, m_&chi;=200 GeV, and sin&theta;=0.01.

Expected-1&sigma; exclusion contour at 95&#37; C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_&chi;=1, m_&chi;=200 GeV, and sin&theta;=0.01.

Expected+2&sigma; exclusion contour at 95&#37; C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_&chi;=1, m_&chi;=200 GeV, and sin&theta;=0.01.

Expected-2&sigma; exclusion contour at 95&#37; C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_&chi;=1, m_&chi;=200 GeV, and sin&theta;=0.01.

Observed upper limits at 95&#37; C.L. on &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) for m_Z'=0.5 TeV as a function of m_s. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) process for m_Z'=0.5 TeV, shown in dashed blue.

Observed upper limits at 95&#37; C.L. on &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) for m_Z'=1 TeV as a function of m_s. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) process for m_Z'=1 TeV, shown in dashed blue.

Observed upper limits at 95&#37; C.L. on &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) for m_Z'=1.7 TeV as a function of m_s. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) process for m_Z'=1.7 TeV, shown in dashed blue.

Observed upper limits at 95&#37; C.L. on &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) for m_Z'=2.1 TeV as a function of m_s. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the &sigma;(pp &rarr; s &chi;&chi;) &times; B(s &rarr; W^&pm; W^&mp;) process for m_Z'=2.1 TeV, shown in dashed blue.

Data overlaid on SM background yields stacked in each SR and CR category after the fit to data ('Post-fit'). The yields in the SR are broken down into their contributions to the individual bins. The maximum-likelihood estimators are set to the conditional values of the CR-only fit, and propagated to SR and CRs.

Dominant sources of uncertainty for three dark Higgs scenarios after the fit to data. The uncertainties are quantified in terms of their contribution to the fitted signal uncertainty that is expressed relative to the theory prediction. Three representative dark Higgs signal scenarios with g_q=0.25, g_&chi;=1.0, sin&theta;=0.01 and m_&chi;=200 GeV are considered, which are indicated using the (m_Z', m_s) format in units of GeV in the table columns.

Cumulative efficiencies in the merged category for three representative dark Higgs signal scenarios with g_q=0.25, g_&chi;=1.0, sin&theta;=0.01, m_Z' = 1 TeV, and m_&chi;=200 GeV considering s&rarr;W(&ell;&nu;)W(qq) decays only.

Cumulative efficiencies in the resolved category for three representative dark Higgs signal scenarios with g_q=0.25, g_&chi;=1.0, sin&theta;=0.01, m_Z' = 1 TeV, and m_&chi;=200 GeV considering s&rarr;W(&ell;&nu;)W(qq) decays only.

Theoretical cross section for &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) for each of the dark Higgs signal points at m_Z′ ={300, 350, 400, 500, 750} GeV, with g_q = 0.25, g<sub>&chi; = 1.0, sin&theta; = 0.01, m_Z′ = 1 TeV , and m_&chi; = 200 GeV. Also shown are experimentally excluded cross sections of &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) (Obs.) together with the expectations (Exp.) varied up and down by one standard deviation (&plusmn;1&sigma;).

Theoretical cross section for &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) for each of the dark Higgs signal points at m_Z′ ={1000, 1700} GeV, with g_q = 0.25, g<sub>&chi; = 1.0, sin&theta; = 0.01, m_Z′ = 1 TeV , and m_&chi; = 200 GeV. Also shown are experimentally excluded cross sections of &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) (Obs.) together with the expectations (Exp.) varied up and down by one standard deviation (&plusmn;1&sigma;).

Theoretical cross section for &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) for each of the dark Higgs signal points at m_Z′ ={2100, 2500, 2900, 3300} GeV, with g_q = 0.25, g<sub>&chi; = 1.0, sin&theta; = 0.01, m_Z′ = 1 TeV , and m_&chi; = 200 GeV. Also shown are experimentally excluded cross sections of &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) (Obs.) together with the expectations (Exp.) varied up and down by one standard deviation (&plusmn;1&sigma;).

Expected CLs values in the High mA SR, mA vs ma signal grid.

Observed CLs values in the High mA SR, mA vs ma signal grid.

Expected CLs values in the Low mA SR, mA vs ma signal grid.

Observed CLs values in the High mA SR, mA vs ma signal grid.

CLs+b values in the Low mA SR, mA vs tanB signal grid.

CLs+b values in the High mA SR, mA vs ma signal grid.

CLs+b values in the Low mA SR, mA vs ma signal grid.

Cut flow of the 2HDM+a signal points, gluon–gluon fusion production, Low mA SR. tanB = 1, $sin\theta$ = 0.35. The first two entries in the tables are number of raw MC events, third entry is theoretical prediction, and all other lines include the correct weights. Note that during the generation Higgs boson’s branching ratio to taus has been set to 1. An additional factor of 0.0627 is used to account for that, starting from the ‘Initial’ entry.

Cut flow of the 2HDM+a signal points, gluon–gluon fusion production, High mA SR. tanB = 1, $sin\theta$ = 0.35. The first two entries in the tables are number of raw MC events, third entry is theoretical prediction, and all other lines include the correct weights. Note that during the generation Higgs boson’s branching ratio to taus has been set to 1. An additional factor of 0.0627 is used to account for that, starting from the ‘Initial’ entry.

Cut flow of the 2HDM+a signal points, bb annihilation production, Low mA SR. tanB = 1, $sin\theta$ = 0.35. The first two entries in the tables are number of raw MC events, third entry is theoretical prediction, and all other lines include the correct weights. Note that during the generation Higgs boson’s branching ratio to taus has been set to 1. An additional factor of 0.0627 is used to account for that, starting from the ‘Initial’ entry.

Cut flow of the 2HDM+a signal points, bb annihilation production, High mA SR. tanB = 1, $sin\theta$ = 0.35. The first two entries in the tables are number of raw MC events, third entry is theoretical prediction, and all other lines include the correct weights. Note that during the generation Higgs boson’s branching ratio to taus has been set to 1. An additional factor of 0.0627 is used to account for that, starting from the ‘Initial’ entry.

A comparison of the observed and expected yields in the four bins of the Low mA SR.

A comparison of the observed and expected yields in the two bins of the High mA SR.

Expected exclusion contours at 95% CL as a function of mA and ma.

Observed exclusion contours at 95% CL as a function of mA and ma.

Expected +- 1sigma exclusion contours at 95% CL as a function of mA and ma.

Expected exclusion contours at 95% CL as a function of mA and ma.

Observed exclusion contours at 95% CL as a function of mA and ma.

Expected +- 1sigma exclusion contours at 95% CL as a function of mA and ma.

Acceptance, High mA SR, mA vs tanB grid, 400-750 GeV, bb prod

Acceptance, High mA SR, mA vs tanB grid, >750 GeV, bb prod

Acceptance, Low mA SR, mA vs tanB grid, 100-250 GeV, bb prod

Acceptance, Low mA SR, mA vs tanB grid, 250-400 GeV, bb prod

Acceptance, Low mA SR, mA vs tanB grid, 400-550 GeV, bb prod

Acceptance, Low mA SR, mA vs tanB grid, >550 GeV, bb prod

Acceptance, High mA SR, mA vs ma grid, 400-750 GeV, bb prod

Acceptance, High mA SR, mA vs ma grid, >750 GeV, bb prod

Acceptance, Low mA SR, mA vs ma grid, 100-250 GeV, bb prod

Acceptance, Low mA SR, mA vs ma grid, 250-400 GeV, bb prod

Acceptance, Low mA SR, mA vs ma grid, 400-550 GeV, bb prod

Acceptance, Low mA SR, mA vs ma grid, >550 GeV, bb prod

Acceptance, High mA SR, mA vs tanB grid, 400-750 GeV, ggF prod

Acceptance, High mA SR, mA vs tanB grid, >750 GeV, ggF prod

Acceptance, Low mA SR, mA vs tanB grid, 100-250 GeV, ggF prod

Acceptance, Low mA SR, mA vs tanB grid, 250-400 GeV, ggF prod

Acceptance, Low mA SR, mA vs tanB grid, 400-550 GeV, ggF prod

Acceptance, Low mA SR, mA vs tanB grid, >550 GeV, ggF prod

Acceptance, High mA SR, mA vs ma grid, 400-750 GeV, ggF prod

Acceptance, High mA SR, mA vs ma grid, >750 GeV, ggF prod

Acceptance, Low mA SR, mA vs ma grid, 100-250 GeV, ggF prod

Acceptance, Low mA SR, mA vs ma grid, 250-400 GeV, ggF prod

Acceptance, Low mA SR, mA vs ma grid, 400-550 GeV, ggF prod

Acceptance, Low mA SR, mA vs ma grid, >550 GeV, ggF prod

Efficiency, High mA SR, mA vs tanB grid, 400-750 GeV, bb prod

Efficiency, High mA SR, mA vs tanB grid, >750 GeV, bb prod

Efficiency, Low mA SR, mA vs tanB grid, 100-250 GeV, bb prod

Efficiency, Low mA SR, mA vs tanB grid, 250-400 GeV, bb prod

Efficiency, Low mA SR, mA vs tanB grid, 400-550 GeV, bb prod

Efficiency, Low mA SR, mA vs tanB grid, >550 GeV, bb prod

Efficiency, High mA SR, mA vs ma grid, 400-750 GeV, bb prod

Efficiency, High mA SR, mA vs ma grid, >750 GeV, bb prod

Efficiency, Low mA SR, mA vs ma grid, 100-250 GeV, bb prod

Efficiency, Low mA SR, mA vs ma grid, 250-400 GeV, bb prod

Efficiency, Low mA SR, mA vs ma grid, 400-550 GeV, bb prod

Efficiency, Low mA SR, mA vs ma grid, >550 GeV, bb prod

Efficiency, High mA SR, mA vs tanB grid, 400-750 GeV, ggF prod

Efficiency, High mA SR, mA vs tanB grid, >750 GeV, ggF prod

Efficiency, Low mA SR, mA vs tanB grid, 100-250 GeV, ggF prod

Efficiency, Low mA SR, mA vs tanB grid, 250-400 GeV, ggF prod

Efficiency, Low mA SR, mA vs tanB grid, 400-550 GeV, ggF prod

Efficiency, Low mA SR, mA vs tanB grid, >550 GeV, ggF prod

Efficiency, High mA SR, mA vs ma grid, 400-750 GeV, ggF prod

Efficiency, High mA SR, mA vs ma grid, >750 GeV, ggF prod

Efficiency, Low mA SR, mA vs ma grid, 100-250 GeV, ggF prod

Efficiency, Low mA SR, mA vs ma grid, 250-400 GeV, ggF prod

Efficiency, Low mA SR, mA vs ma grid, 400-550 GeV, ggF prod

Efficiency, Low mA SR, mA vs ma grid, >550 GeV, ggF prod

Observed excluded cross-section at 95% CL for the WZ-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production from Fig 8(aux).

Expected exclusion limits at 95% CL for the WZ-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production.

Observed exclusion limits at 95% CL for the Wh-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production from from Fig 13(a) and from Fig 7 and Fig 10(aux).

Observed excluded cross-section at 95% CL for the Wh-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production from Fig 7(aux) and Fig 10(aux).

positive one $\sigma$ observed exclusion limits at 95% CL for the Wh-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production from from Fig 13(a) and from Fig 7 and Fig 10(aux).

negative one $\sigma$ observed exclusion limits at 95% CL for the Wh-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production from from Fig 13(a) and from Fig 7 and Fig 10(aux).

Expected exclusion limits at 95% CL for the Wh-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production.

Expected exclusion limits at 95% CL for the Wh-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production.

Expected exclusion limits at 95% CL for the Wh-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production.

Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region $SR^{bRPV}_{2l-SS}$. in a susy scenario where $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are produced in pairs and decay to all possible allowed bRPV decays. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{0} _{1}/\tilde{\chi}^{0} _{2})$ = 200 GeV, tan$\beta$=5. Only statistical uncertainties are shown.

Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region $SR^{bRPV}_{3l}$. in a susy scenario where $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are produced in pairs and decay to all possible allowed bRPV decays. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{0} _{1}/\tilde{\chi}^{0} _{2})$ = 200 GeV, tan$\beta$=5. Only statistical uncertainties are shown.

Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region $SR^{WZ}_{high-m_{T2}}$. The wino-like doublet pair ($\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$) were produced and then decays into $bino-like \tilde{\chi}^{0} _{1}$ which is the lightest SUSY particle (LSP) accompanied by mass on-shell or mass off-shell W and Z bosons. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{\pm} _{1}/\tilde{\chi}^{0} _{2})$ = 150 GeV, $m(\tilde{\chi}^{0} _{1})$ = 50 GeV. Only statistical uncertainties are shown.

Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region $SR^{WZ}_{low-m_{T2}}$. The wino-like doublet pair ($\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$) were produced and then decays into $bino-like \tilde{\chi}^{0} _{1}$ which is the lightest SUSY particle (LSP) accompanied by mass on-shell or mass off-shell W and Z bosons. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{\pm} _{1}/\tilde{\chi}^{0} _{2})$ = 150 GeV, $m(\tilde{\chi}^{0} _{1})$ = 50 GeV. Only statistical uncertainties are shown.

Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the low mass $SR^{RPV}_{2l1b}$, where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{0} _{1}/\tilde{\chi}^{0} _{2})$ = 200 GeV. Only statistical uncertainties are shown.

Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the medium mass $SR^{RPV}_{2l1b}$, where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{0} _{1}/\tilde{\chi}^{0} _{2})$ = 200 GeV. Only statistical uncertainties are shown.

Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the low mass $SR^{RPV}_{2l2b}$, where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{0} _{1}/\tilde{\chi}^{0} _{2})$ = 200 GeV. Only statistical uncertainties are shown.

Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the medium mass $SR^{RPV}_{2l2b}$, where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{0} _{1}/\tilde{\chi}^{0} _{2})$ = 200 GeV. Only statistical uncertainties are shown.

Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the high mass $SR^{RPV}_{2l2b}$, where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{0} _{1}/\tilde{\chi}^{0} _{2})$ = 200 GeV. Only statistical uncertainties are shown.

Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the low mass $SR^{RPV}_{2l3b}$, where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{0} _{1}/\tilde{\chi}^{0} _{2})$ = 200 GeV. Only statistical uncertainties are shown.

Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the medium mass $SR^{RPV}_{2l3b}$, where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{0} _{1}/\tilde{\chi}^{0} _{2})$ = 200 GeV. Only statistical uncertainties are shown.

Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the high mass $SR^{RPV}_{2l3b}$, where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{0} _{1}/\tilde{\chi}^{0} _{2})$ = 200 GeV. Only statistical uncertainties are shown.

Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the $SR^{Wh}_{low-m_{T2} }$. The wino-like doublet pair ($\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$) were produced and then decays into $bino-like \tilde{\chi}^{0} _{1}$ which is the lightest SUSY particle (LSP) accompanied by mass on-shell or mass off-shell W and Higgs bosons. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{\pm} _{1}/\tilde{\chi}^{0} _{2})$ = 300 GeV, $m(\tilde{\chi}^{0} _{1})$ = 100 GeV. Only statistical uncertainties are shown.

Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the $SR^{Wh}_{high-m_{T2} }$. The wino-like doublet pair ($\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$) were produced and then decays into $bino-like \tilde{\chi}^{0} _{1}$ which is the lightest SUSY particle (LSP) accompanied by mass on-shell or mass off-shell W and Higgs bosons. The masses of the superpartners involved in the process are set to $m(\tilde{\chi}^{\pm} _{1}/\tilde{\chi}^{0} _{2})$ = 300 GeV, $m(\tilde{\chi}^{0} _{1})$ = 100 GeV. Only statistical uncertainties are shown.

Signal Hepdataeptance for $SR^{bRPV}_{2l-SS}$ signal region from Fig 13(a)(aux) in a SUSY scenario where $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are produced in pairs and decay to all possible allowed bRPV decays.

Signal Hepdataeptance for $SR^{bRPV}_{3l}$ signal region from Fig 13(b)(aux) in a SUSY scenario where $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are produced in pairs and decay to all possible allowed bRPV decays.

Signal acceptance for $SR^{WZ}_{high-m_{T2}}$ in a SUSY scenario where the wino-like doublet pair ($\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$) were produced and then decays into $bino-like \tilde{\chi}^{0} _{1}$ which is the lightest SUSY particle (LSP) accompanied by mass on-shell or mass off-shell W and Z bosons.

Signal acceptance for $SR^{WZ}_{low-m_{T2}}$ in a SUSY scenario where the wino-like doublet pair ($\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$) were produced and then decays into $bino-like \tilde{\chi}^{0} _{1}$ which is the lightest SUSY particle (LSP) accompanied by mass on-shell or mass off-shell W and Z bosons.

Signal acceptance for $SR^{RPV}_{2l1b}-L$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.

Signal acceptance for $SR^{RPV}_{2l1b}-M$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.

Signal acceptance for $SR^{RPV}_{2l2b}-L$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.

Signal acceptance for $SR^{RPV}_{2l2b}-M$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.

Signal acceptance for $SR^{RPV}_{2l2b}-H$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.

Signal acceptance for $SR^{RPV}_{2l3b}-L$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.

Signal acceptance for $SR^{RPV}_{2l3b}-M$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.

Signal acceptance for $SR^{RPV}_{2l3b}-H$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.

Signal efficiency for $SR^{bRPV}_{2l-SS}$ signal region in a SUSY scenario where $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are produced in pairs and decay to all possible allowed bRPV decays.

Signal efficiency for $SR^{bRPV}_{3l}$ signal region in a SUSY scenario where $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are produced in pairs and decay to all possible allowed bRPV decays.

Signal efficiency for $SR^{WZ}_{high-m_{T2}}$ in a SUSY scenario where the wino-like doublet pair ($\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$) were produced and then decays into $bino-like \tilde{\chi}^{0} _{1}$ which is the lightest SUSY particle (LSP) accompanied by mass on-shell or mass off-shell W and Z bosons.

Signal efficiency for $SR^{WZ}_{low-m_{T2}}$ in a SUSY scenario where the wino-like doublet pair ($\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$) were produced and then decays into $bino-like \tilde{\chi}^{0} _{1}$ which is the lightest SUSY particle (LSP) accompanied by mass on-shell or mass off-shell W and Z bosons.

Signal efficiency for $SR^{RPV}_{2l1b}-L$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.

Signal efficiency for $SR^{RPV}_{2l1b}-M$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.

Signal efficiency for $SR^{RPV}_{2l2b}-L$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.

Signal efficiency for $SR^{RPV}_{2l2b}-M$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.

Signal efficiency for $SR^{RPV}_{2l2b}-H$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.

Signal efficiency for $SR^{RPV}_{2l3b}-L$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.

Signal efficiency for $SR^{RPV}_{2l3b}-M$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.

Signal efficiency for $SR^{RPV}_{2l3b}-H$ signal region in a SUSY scenario where the $\tilde{\chi}^{0} _{1} and \tilde{\chi}^{0} _{2}$ are directly produced and undergoes prompt RPV decays.

Signal acceptance for $SR^{Wh}_{high-m_{T2} }$ signal region from Fig 11(a)(aux) in a SUSY scenario where direct production of a lightest $\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$ , decay with 100% branching ratio to a final state with a same sign light lepton (e or $\mu$) pair and two lightest neutralino1, via the on-shell emission of SM W and Higgs bosons,

Signal acceptance for $SR^{Wh}_{low-m_{T2} }$ signal region from Fig 11(b)(aux) in a SUSY scenario where direct production of a lightest $\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$ , decay with 100% branching ratio to a final state with a same sign light lepton (e or $\mu$) pair and two lightest neutralino1, via the on-shell emission of SM W and Higgs bosons,

Signal efficiency for $SR^{Wh}_{high-m_{T2} }$ signal region from Fig 15(a)(aux) in a SUSY scenario where direct production of a lightest $\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$ , decay with 100% branching ratio to a final state with a same sign light lepton (e or $\mu$) pair and two lightest neutralino1, via the on-shell emission of SM W and Higgs bosons,

Signal efficiency for $SR^{Wh}_{low-m_{T2} }$ signal region from Fig 15(b)(aux) in a SUSY scenario where direct production of a lightest $\tilde{\chi}^{\pm} _{1} and \tilde{\chi}^{0} _{2}$ , decay with 100% branching ratio to a final state with a same sign light lepton (e or $\mu$) pair and two lightest neutralino1, via the on-shell emission of SM W and Higgs bosons,

Observed 95% X-section upper limits as a function of higgsino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{1}/\tilde{\chi}^{0}_{2}$ mass in the bilinear RPV model from Fig 14.

Observed 95% X-section upper limits as a function of higgsino $\tilde{\chi}^{0}_{1}/\tilde{\chi}^{0}_{2}$ mass in the UDD RPV model from Fig 18.

Observed 95% X-section upper limits as a function of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ mass in the WZ-mediated simplified model of wino $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ production from Fig 9(aux).

N-1 distributions for $m_{T2}$ of observed data and expected background towards $SR^{WZ}_{high-m_{T2}}$ from publication's Figure 11(a) . The last bin is inclusive.

N-1 distributions for $m_{T2}$ of observed data and expected background towards $SR^{WZ}_{low-m_{T2}}$ from publication's Figure 11(b) . The last bin is inclusive.

N-1 distributions for $m_{T2}$ of observed data and expected background towards $SR^{bRPV}_{2l-SS}$ from publication's Figure 11(c) . The last bin is inclusive.

N-1 distributions for $m_{T2}$ of observed data and expected background towards $SR^{bRPV}_{3l}$ from publication's Figure 11(d) . The last bin is inclusive.

N-1 distributions for $\sum p^{b-jet}_{T}/\sum p^{jet}_{T}$ of observed data and expected background towards $SR^{RPV}_{2l1b}-L$ from publication's Figure 16(a) . The last bin is inclusive.

N-1 distributions for $\sum p^{b-jet}_{T}/\sum p^{jet}_{T}$ of observed data and expected background towards $SR^{RPV}_{2l2b}-M$ from publication's Figure 16(b) . The last bin is inclusive.

N-1 distributions for $\sum p^{b-jet}_{T}/\sum p^{jet}_{T}$ of observed data and expected background towards $SR^{RPV}_{2l3b}-H$ from publication's Figure 16(c) . The last bin is inclusive.

N-1 distribution for $E_{T}^{miss}$ in $SR^{Wh}_{high-m_{T2} }$ in ee channel

N-1 distribution for $E_{T}^{miss}$ in $SR^{Wh}_{high-m_{T2} }$ in e$\mu$ channel

N-1 distribution for $E_{T}^{miss}$ in $SR^{Wh}_{high-m_{T2} }$ in $\mu\mu$ channel

N-1 distribution for $\mathcal{S}(E_{T}^{miss})$ in $SR^{Wh}_{low-m_{T2} }$ in ee channel

N-1 distribution for $\mathcal{S}(E_{T}^{miss})$ in $SR^{Wh}_{low-m_{T2} }$ in e$\mu$ channel

N-1 distribution for $\mathcal{S}(E_{T}^{miss})$ in $SR^{Wh}_{low-m_{T2} }$ in $\mu\mu$ channel

Lévy scale parameter $R$ versus average $m_T$ of the pair. The graphical representation of statistical and systematic uncertainties is the same as in Fig. 4.

Lévy index parameter $\alpha$ versus average $m_T$ of the pair. Statistical and systematic uncertainties are indicated similarly to Fig. 4. The horizontal line, %\aplha$ = 1.207, represents the 0%-30% centrality average value of %\aplha$.

Inverse square of the Lévy scale parameter 1/$R^2$ versus average $m_T$ of the pair. Statistical and systematic uncertainties shown as bars and boxes, respectively.

New scale parameter $\hat{R}$ versus average $m_T$ of the pair, with a linear fit. Statistical and systematic uncertainties shown as bars and boxes, respectively.

Normalized correlation strength parameter $\lambda/\lambda_{max}$ versus average $m_T$ of the pair. The data are compared with parameter scans from Refs. [58, 59] using different values of in-medium $\eta^{\prime}$ mass $m^{*}_{\eta^{\prime}}$ and slope parameter $B^{-1}_{\eta^{\prime}}$ . A best fit with Eq. (63) and the resulting $H$ and $\sigma$ parameters are also shown.

Normalized correlation strength parameter $\lambda/\lambda_{max}$ versus average $m_T$ of the pair. The data are compared with parameter scans from Refs. [58, 59] using different values of in-medium $\eta^{\prime}$ mass $m^{*}_{\eta^{\prime}}$ and slope parameter $B^{-1}_{\eta^{\prime}}$ . A best fit with Eq. (63) and the resulting $H$ and $\sigma$ parameters are also shown.

Normalized correlation strength parameter $\lambda/\lambda_{max}$ versus average $m_T$ of the pair. The data are compared with parameter scans from Refs. [58, 59] using different values of in-medium $\eta^{\prime}$ mass $m^{*}_{\eta^{\prime}}$ and slope parameter $B^{-1}_{\eta^{\prime}}$ . A best fit with Eq. (63) and the resulting $H$ and $\sigma$ parameters are also shown.

Normalized correlation strength parameter $\lambda/\lambda_{max}$ versus average $m_T$ of the pair. The data are compared with parameter scans from Refs. [58, 59] using different values of in-medium $\eta^{\prime}$ mass $m^{*}_{\eta^{\prime}}$ and slope parameter $B^{-1}_{\eta^{\prime}}$ . A best fit with Eq. (63) and the resulting $H$ and $\sigma$ parameters are also shown.

Normalized correlation strength parameter $\lambda/\lambda_{max}$ versus average $m_T$ of the pair. The data are compared with parameter scans from Refs. [58, 59] using different values of in-medium $\eta^{\prime}$ mass $m^{*}_{\eta^{\prime}}$ and slope parameter $B^{-1}_{\eta^{\prime}}$ . A best fit with Eq. (63) and the resulting $H$ and $\sigma$ parameters are also shown.

Dependence of $\overline{p}$-$\overline{d}$ correlation on pseudorapidity acceptance of $\overline{p}$ and $\overline{d}$ selection in Pb-Pb collisions at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV. Results are for 20.0--30.0$\%$ collision centrality.

Dependence of $\overline{p}$-$\overline{d}$ correlation on pseudorapidity acceptance of $\overline{p}$ and $\overline{d}$ selection in Pb-Pb collisions at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV. Results are for 50.0--60.0$\%$ collision centrality.

HBT parameters of charge-combined kaon pairs, shown as value $\pm$ statistical uncertainty [absolute value] $\pm$ systematic uncertainty [%] for the centrality bins shown in Fig. 3.

$R_o/R_s$ of positive pion pairs shown as value $\pm$ statistical uncertainty [absolute value] $\pm$ systematic uncertainty [%] for the centrality bins shown in Fig. 4.

$R_o/R_s$ of negative pion pairs shown as value $\pm$ statistical uncertainty [absolute value] $\pm$ systematic uncertainty [%] for the centrality bins shown in Fig. 4.

$R_o/R_s$ of charge-combined kaon pairs shown as value $\pm$ statistical uncertainty [absolute value] $\pm$ systematic uncertainty [%] for the centrality bins shown in Fig. 4.

Azimuthal angle dependence of HBT radii of charged kaons, shown as value $\pm$ statistical uncertainty $\pm$ systematic uncertainty for the centrality bins shown in Fig. 8.

Azimuthal angle dependence of HBT radii of charged pions, shown as value $\pm$ statistical uncertainty $\pm$ systematic uncertainty for the 0%–20% and 20%–60% centrality bins.

Oscillation amplitudes relative to the event plane for charged pions, shown as value $\pm$ statistical uncertainty $\pm$ systematic uncertainty for the 0%–20% and 20%–60% centrality bins.

Oscillation amplitudes relative to the event plane for charged kaons, shown as value $\pm$ statistical uncertainty $\pm$ systematic uncertainty for the 0%–20% and 20%–60% centrality bins.

Summary of extracted parameters in the blast-wave model fit for two fitting conditions. The $T_f$ and $\rho_0$ parameters were obtained by fits to $p_T$ spectra, and the other parameters were obtained by a simultaneous fit to $v_2$ and the HBT radii.

Cutflow for two representative signal samples used in this analysis. The excited tau mass $m_{\tau^\ast} = 2.75$ TeV and the contact interaction scale $\Lambda=10$ TeV. The LQ mass $m_\mathrm{LQ} = 1.3$ TeV. The event yields include all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Acceptance x efficiency of the $\tau^\ast$ signal SR selection

Acceptance x efficiency of the LQ signal SR selection

Distribution of ${E}_{T}^{miss}$ after the fit of the multijet backgrounds, in the electron channel, in the $t\bar{t}$ 3-jets VR, without applying the cut on ${E}_{T}^{miss}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of ${E}_{T}^{miss}$ after the fit of the multijet backgrounds, in the electron channel, in the $t\bar{t}$ 4-jets VR, without applying the cut on ${E}_{T}^{miss}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of $m_{T}^{W}$ after the fit of the multijet backgrounds, in the muon channel, in the signal region, without applying the cut on $m_{T}^{W}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of $m_{T}^{W}$ after the fit of the multijet backgrounds, in the muon channel, in the $W$+jets VR, without applying the cut on $m_{T}^{W}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of $m_{T}^{W}$ after the fit of the multijet backgrounds, in the muon channel, in the $t\bar{t}$ 3-jets VR, without applying the cut on $m_{T}^{W}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of $m_{T}^{W}$ after the fit of the multijet backgrounds, in the muon channel, in the $t\bar{t}$ 4-jets VR, without applying the cut on $m_{T}^{W}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Expected distributions of the MEM discriminant $P(S|X)$ in the SR, for the s-channel single-top signal, and for the $t\bar{t}$ and $W$+jets backgrounds, for MEM discriminant values larger than $2.0\times10^{-4}$. Each distribution is normalised to unity. The binning is the same as the optimised binning used in the signal extraction fit, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the $W$+jets VR. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit presented in Section 5 in the paper. The uncertainty band indicates the simulation's statistical uncertainty and the normalisation uncertainties for the various processes in each bin, summed in quadrature. The ratio of the observed number to the predicted number of events in each bin is shown in the lower panel of the figure, with different vertical axis ranges. The binning is the same as the optimised binning used in the signal extraction fit described in Section 8 in the paper, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the $t\bar{t}$ 3-jets VR. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit presented in Section 5 in the paper. The uncertainty band indicates the simulation's statistical uncertainty and the normalisation uncertainties for the various processes in each bin, summed in quadrature. The ratio of the observed number to the predicted number of events in each bin is shown in the lower panel of the figure, with different vertical axis ranges. The binning is the same as the optimised binning used in the signal extraction fit described in Section 8 in the paper, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the $t\bar{t}$ 4-jets VR. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit presented in Section 5 in the paper. The uncertainty bands indicate the simulation's statistical uncertainty and the normalisation uncertainties for the various processes in each bin, summed in quadrature. The ratio of the observed number to the predicted number of events in each bin is shown in the lower panel of the figure, with different vertical axis ranges. The binning is the same as the optimised binning used in the signal extraction fit described in Section 8 in the paper, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the SR before the fit to data, for MEM discriminant values larger than $2.0\times10^{-4}$. The lower panel of the figure shows the ratio of the data to the prediction, with different vertical axis ranges. The uncertainty band indicates the total uncertainties and their correlations in each bin. The uncertainties in the $t\bar{t}$ and $W$+jets normalisation factors, as well as in the s-channel signal cross-section, are not defined pre-fit and therefore not included. The binning is the same as the optimised binning used in the fit, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the SR after the fit to data, for MEM discriminant values larger than $2.0\times10^{-4}$. The lower panel of the figure shows the ratio of the data to the prediction, with different vertical axis ranges. The uncertainty band indicates the total uncertainties and their correlations in each bin. The binning is the same as the optimised binning used in the fit, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the SR after the fit to data, for MEM discriminant values larger than $2.0\times10^{-4}$, after subtraction of all backgrounds. The fitted distribution for the simulation of the signal is shown together with the post-fit uncertainty in the backgrounds. The binning is the same as the optimised binning used in the fit, resulting in a non-linear horizontal scale.

Pre-fit and post-fit event yields in the SR, for MEM discriminant values larger than $2.0\times10^{-4}$. The central value of the event yield for each process is calculated by summing the values of the discriminant bin contents, using the nominal expected yield for the pre-fit value, and the best-fit estimate for the post-fit value. The error includes statistical and systematic uncertainties summed in quadrature. All sources of systematic uncertainties are included, taking into account correlations and anti-correlations in the post-fit case. The uncertainties in the $t\bar{t}$ and $W$+jets normalisation factors, as well as in the s-channel signal cross-section, are not defined pre-fit and therefore only included in the post-fit uncertainties.

Observed impact of the different sources of uncertainty on the measured s-channel signal cross-section, grouped by categories. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. The 'Systematic uncertainties' category combines all sources of systematic uncertainties. The statistical uncertainty is obtained by repeating the fit after having fixed all nuisance parameters, including the $t\bar{t}$ and $W$+jets normalisation factors. 'Total' gives the total uncertainty on the measurement.

Observed impact of the different sources of $t\bar{t}$ modelling uncertainty on the measured s-channel signal cross-section. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. 'PS & had.' refers to the parton shower and hadronisation model, and 'ME/PS matching' to the matching of the ME to the parton shower.

Observed impact of the different sources of s-channel modelling uncertainty on the measured s-channel signal cross-section. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. 'PS & had.' refers to the parton shower and hadronisation model, as described in Section 7 in the paper.

Observed impact of the different sources of t-channel modelling uncertainty on the measured s-channel signal cross-section. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. 'PS & had.' refers to the parton shower and hadronisation model, as described in Section 7 in the paper.

Observed impact of the different sources of $tW$ modelling uncertainty on the measured s-channel signal cross-section, grouped by categories. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. 'PS & had.' refers to the parton shower and hadronisation model, and '$t\bar{t}$ overlap' to the algorithm removing the overlap between $tW$ and $t\bar{t}$ production at NLO, as described in Section 7 in the paper.

Observed impact of the different sources of PDF uncertainties on the measured s-channel signal cross-section, grouped by categories. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root.

Comparison between data and prediction after the fit to data in the signal region for the leading-jet $p_{T}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the leading-jet $\eta$. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the subleading-jet $p_{T}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the subleading-jet $\eta$. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the lepton $p_{T}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the lepton $\eta$. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the ${E}_{T}^{miss}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the $m_{T}^{W}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Nuisance parameters ranked according to their post-fit impacts on the best-fit value of the ratio $\mu$ of the measured cross-section to the predicted cross-section. In the figure, only the 20 nuisance parameters with the largest post-fit impacts are shown. The empty (solid) blue rectangles illustrate the pre-fit (post-fit) impact on $\mu$, corresponding to the upper axis. The pre-fit (post-fit) impact of each nuisance parameter, $\Delta\mu$, is calculated as the difference in the fitted value of $\mu$ between the nominal fit and the fit when fixing the corresponding nuisance parameter to $\hat{\theta}\pm\Delta\theta$ ($\hat{\theta}\pm\Delta\hat{\theta}$), where $\hat{\theta}$ is the best-fit value of the nuisance parameter and $\Delta\theta$ ($\Delta\hat{\theta}$) is its pre-fit (post-fit) uncertainty. Several systematic uncertainties are split into different nuisance parameters, which are indicated by NP. JES (JER) indicates jet energy scale (resolution), and $\gamma$ indicates a nuisance parameter associated to the MC statistics in one of the 18 bins numbered from 0 to 17. The black points show the best-fit values of the nuisance parameters, with the error bars representing the post-fit uncertainties. Each nuisance parameter is shown wrt. its nominal value, $\theta_0$, and in units of its pre-fit uncertainty, except the free-floating normalisation factors of the $t\bar{t}$ and $W$+jets backgrounds, and the parameters associated to the MC statistics in each bin, for which the post-fit values and uncertainties are shown.

Correlation matrix of the nuisance parameters and of the ratio $\mu$ of the measured cross-section to the predicted cross-section. The correlations are given after the fit to data. In the figure, only the parameters which have a correlation of at least 0.2 with any other parameter are shown.

Distribution of the MEM discriminant $P(S|X)$ in the SR for MEM discriminant values larger than $2.0\times10^{-4}$, for the collision data used for the measurement, and for 1000 pseudo-data replicas, generated using a bootstrapping technique, in order to assess the statistical correlations between this measurement and others, for the purpose of combinations. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the <a href="https://zenodo.org/record/5361038">BootstrapGenerator</a> software package , which implements a technique described in <a href="https://cds.cern.ch/record/2759945/">ATL-PHYS-PUB-2021-011</a>. The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. Each pseudo-data replica is assigned an index, ranging from 0 to 999, corresponding to the random number index used consistently for each observed data event.

Measured values of the signal cross-section and of the $t\bar{t}$ and $W$+jets normalisation factors, obtained by statistical-only fits to the collision data used for the measurement, and to 1000 pseudo-data replicas, generated using a bootstrapping technique, in order to assess the statistical correlations between this measurement and others, for the purpose of combinations. The central values and their statistical uncertainties are obtained by repeating the fit after having fixed all nuisance parameters, except the $t\bar{t}$ and $W$+jets normalisation factors, which are let free-floating (unlike for the statistical uncertainty on the cross-section quoted in the paper). The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the <a href="https://zenodo.org/record/5361038">BootstrapGenerator</a> software package , which implements a technique described in <a href="https://cds.cern.ch/record/2759945/">ATL-PHYS-PUB-2021-011</a>. The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. Each pseudo-data replica is assigned an index, ranging from 0 to 999, corresponding to the random number index used consistently for each observed data event.

Expected, with the corresponding $\pm 1\sigma$ intervals, and observed 95% CL branching fraction upper limits for the Higgs and $Z$ boson decays into a quarkonium state and a photon. Standard Model production of the Higgs boson is assumed. The corresponding upper limits on the production cross section times branching fraction $\sigma\times\mathcal{B}$ are also shown.