Invariant $p_{T}$-differential spectra of $\Lambda + \bar{\Lambda}$ in p+p and p+Pb at $\sqrt{s}$=5.02 TeV in various $y_{CM}$ ranges

Invariant $p_{T}$-differential spectra of $\Xi- + \bar{\Xi+}$ in p+p and p+Pb at $\sqrt{s}$=5.02 TeV in various |$y_{CM}$| ranges

Invariant $p_{T}$-differential spectra of $\Xi- + \bar{\Xi+}$ in p+p and p+Pb at $\sqrt{s}$=5.02 TeV in various $y_{CM}$ ranges

Invariant $p_{T}$-differential spectra of $\Omega- + \bar{\Omega+}$ in p+p and p+Pb at $\sqrt{s}$=5.02 TeV in |$y_{CM}$| < 1.8

Invariant $p_{T}$-differential spectra of $\Omega- + \bar{\Omega+}$ in p+p and p+Pb at $\sqrt{s}$=5.02 TeV in |$y_{CM}$| < 1.8

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in |$y_{CM}$| < 1.8

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in |$y_{CM}$| < 1.8

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in -1.8 < $y_{CM}$ < 0

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in |$y_{CM}$| < 1.8

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0 < $y_{CM}$ < 1.8

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in |$y_{CM}$| < 1.8

Invariant $p_{T}$-differential spectra of ${K_{0}}^{S}$ in p+Pb at $\sqrt{s}$=5.02 TeV in various $y_{CM}$ ranges

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in |$y_{CM}$| < 1.8

Invariant $p_{T}$-differential spectra of $\Lambda + \bar{\Lambda}$ in p+Pb at $\sqrt{s}$=5.02 TeV in various $y_{CM}$ ranges

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in -1.8 < $y_{CM}$ < 0

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0.3 < $y_{CM}$ < 0.8

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in -1.8 < $y_{CM}$ < 0

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0.8 < $y_{CM}$ < 1.3

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in -1.8 < $y_{CM}$ < 0

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 1.3 < $y_{CM}$ < 1.8

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0 < $y_{CM}$ < 1.8

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0 < $y_{CM}$ < 1.8

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0 < $y_{CM}$ < 1.8

Invariant $p_{T}$-differential spectra of ${K_{0}}^{S}$ in p+Pb at $\sqrt{s}$=5.02 TeV in various $y_{CM}$ ranges

Invariant $p_{T}$-differential spectra of $\Lambda + \bar{\Lambda}$ in p+Pb at $\sqrt{s}$=5.02 TeV in various $y_{CM}$ ranges

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0.3 < |$y_{CM}$| < 0.8

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0.3 < |$y_{CM}$| < 0.8

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0.3 < |$y_{CM}$| < 0.8

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0.8 < |$y_{CM}$| < 1.3

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0.8 < |$y_{CM}$| < 1.3

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0.8 < |$y_{CM}$| < 1.3

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 1.3 < |$y_{CM}$| < 1.8

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 1.3 < |$y_{CM}$| < 1.8

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 1.3 < |$y_{CM}$| < 1.8

$\phi$ meson production cross section $\mathrm{d}^2\sigma/(\mathrm{d}y\mathrm{d}p_\mathrm{T})$ as a function of $p_\mathrm{T}$ at $\sqrt{s}=5.02$ TeV in pp collisions, in the $2.5<y<3$ rapidity interval.

$\phi$ meson production cross section $\mathrm{d}^2\sigma/(\mathrm{d}y\mathrm{d}p_\mathrm{T})$ as a function of $p_\mathrm{T}$ at $\sqrt{s}=5.02$ TeV in pp collisions, in the $3<y<3.25$ rapidity interval.

$\phi$ meson production cross section $\mathrm{d}^2\sigma/(\mathrm{d}y\mathrm{d}p_\mathrm{T})$ as a function of $p_\mathrm{T}$ at $\sqrt{s}=5.02$ TeV in pp collisions, in the $3.25<y<3.5$ rapidity interval.

$\phi$ meson production cross section $\mathrm{d}^2\sigma/(\mathrm{d}y\mathrm{d}p_\mathrm{T})$ as a function of $p_\mathrm{T}$ at $\sqrt{s}=5.02$ TeV in pp collisions, in the $3.5<y<4$ rapidity interval.

$\phi$ meson production cross section $\mathrm{d}^2\sigma/(\mathrm{d}y\mathrm{d}p_\mathrm{T})$ as a function of $p_\mathrm{T}$ at $\sqrt{s}=13$ TeV in pp collisions, in the $2.5<y<2.75$ rapidity interval.

$\phi$ meson production cross section $\mathrm{d}^2\sigma/(\mathrm{d}y\mathrm{d}p_\mathrm{T})$ as a function of $p_\mathrm{T}$ at $\sqrt{s}=13$ TeV in pp collisions, in the $2.75<y<3$ rapidity interval.

$\phi$ meson production cross section $\mathrm{d}^2\sigma/(\mathrm{d}y\mathrm{d}p_\mathrm{T})$ as a function of $p_\mathrm{T}$ at $\sqrt{s}=13$ TeV in pp collisions, in the $3<y<3.25$ rapidity interval.

$\phi$ meson production cross section $\mathrm{d}^2\sigma/(\mathrm{d}y\mathrm{d}p_\mathrm{T})$ as a function of $p_\mathrm{T}$ at $\sqrt{s}=13$ TeV in pp collisions, in the $3.25<y<3.5$ rapidity interval.

$\phi$ meson production cross section $\mathrm{d}^2\sigma/(\mathrm{d}y\mathrm{d}p_\mathrm{T})$ as a function of $p_\mathrm{T}$ at $\sqrt{s}=13$ TeV in pp collisions, in the $3.5<y<4$ rapidity interval.

$\phi$ meson production cross section $\mathrm{d}^2\sigma/(\mathrm{d}y\mathrm{d}p_\mathrm{T})$ as a function of rapidity at $\sqrt{s}=5.02$ TeV in pp collisions, in the $1<p_T<2$ GeV/$c$ interval.

$\phi$ meson production cross section $\mathrm{d}^2\sigma/(\mathrm{d}y\mathrm{d}p_\mathrm{T})$ as a function of rapidity at $\sqrt{s}=5.02$ TeV in pp collisions, in the $2<p_T<5$ GeV/$c$ interval.

$\phi$ meson production cross section $\mathrm{d}^2\sigma/(\mathrm{d}y\mathrm{d}p_\mathrm{T})$ as a function of rapidity at $\sqrt{s}=5.02$ TeV in pp collisions, in the $5<p_T<8$ GeV/$c$ interval.

$\phi$ meson production cross section $\mathrm{d}^2\sigma/(\mathrm{d}y\mathrm{d}p_\mathrm{T})$ as a function of rapidity at $\sqrt{s}=13$ TeV in pp collisions, in the $1<p_T<2$ GeV/$c$ interval.

$\phi$ meson production cross section $\mathrm{d}^2\sigma/(\mathrm{d}y\mathrm{d}p_\mathrm{T})$ as a function of rapidity at $\sqrt{s}=13$ TeV in pp collisions, in the $2<p_T<5$ GeV/$c$ interval.

$\phi$ meson production cross section $\mathrm{d}^2\sigma/(\mathrm{d}y\mathrm{d}p_\mathrm{T})$ as a function of rapidity at $\sqrt{s}=13$ TeV in pp collisions, in the $5<p_T<10$ GeV/$c$ interval.

$\phi$ meson production integrated cross section as a function of $\sqrt{s}$ for $1.5 < p_\mathrm{T} <5$ GeV/$c$ at forward rapidity in pp collisions.

$p_{\rm T}$-differential $R_{\rm AA}$ of muons from heavy-flavour hadron decays at forward rapidity ($2.5 < y < 4$) for central (0--10%) and semi-central (20--40%) Pb--Pb collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV.

$p_{\rm T}$-differential $R_{\rm AA}$ of muons from heavy-flavour hadron decays at forward rapidity ($2.5 < y < 4$) for peripheral (60--80%) Pb--Pb collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV.

$p_{\rm T}$-differential $R_{\rm AA}$ of muons from heavy-flavour hadron decays at forward rapidity ($2.5 < y < 4$) for central (0--10%) Pb--Pb collisions at $\sqrt{s_{\rm NN}}=2.76$ TeV.

$p_{\rm T}$-differential $R_{\rm AA}$ of muons from heavy-flavour hadron decays at forward rapidity ($2.5 < y < 4$) for central (10--20%) Pb--Pb collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV.

Top quark mass measured inclusive of lepton flavor and for positively charged lepton.

Top quark mass measured inclusive of lepton flavor and for negatively charged lepton.

Top quark mass measured inclusive of lepton flavor and for negatively charged lepton.

Difference between the measured masses of top quarks and antiquarks.

Difference between the measured masses of top quarks and antiquarks.

Ratio of the measured masses of top anitiquarks to quarks.

Ratio of the measured masses of top anitiquarks to quarks.

Comparison of measurements of the top quark mass by ATLAS and CMS Collaborations in various event topologies and center-of-mass energies.

Comparison of measurements of the top quark mass by ATLAS and CMS Collaborations in various event topologies and center-of-mass energies.

Comparison of the mass difference between top quarks and antiquarks measured by ATLAS and CMS colaborations in various event topologies and center-of-mass energies.

Comparison of the mass difference between top quarks and antiquarks measured by ATLAS and CMS colaborations in various event topologies and center-of-mass energies.

Event yields corresponding to positively and negatively charged muons in the final states obtained from simulated signal and background processes, as well as in data for the 2J1T event category.

Event yields corresponding to positively and negatively charged muons in the final states obtained from simulated signal and background processes, as well as in data for the 2J1T event category.

Event yields corresponding to positively and negatively charged electron in the final states obtained from simulated signal and background processes, as well as in data for the 2J1T event category.

Event yields corresponding to positively and negatively charged electron in the final states obtained from simulated signal and background processes, as well as in data for the 2J1T event category.

Summary of systematic uncertainties in GeV for each final-state lepton charge configuration. With the exception of the flavor-dependent JES sources, the total systematic uncertainty is obtained from the sum in quadrature of the individual systematic sources. The statistical uncertainties in the systematic shifts are quoted within parentheses whenever alternative simulated samples with systematic variations have been used.

Summary of systematic uncertainties in GeV for each final-state lepton charge configuration. With the exception of the flavor-dependent JES sources, the total systematic uncertainty is obtained from the sum in quadrature of the individual systematic sources. The statistical uncertainties in the systematic shifts are quoted within parentheses whenever alternative simulated samples with systematic variations have been used.

Correlations in % among the BDT input variables used for the muon final state in signal events of the 2J1T category before application of the decorrelation method available in TMVA.

Correlations in % among the BDT input variables used for the muon final state in signal events of the 2J1T category before application of the decorrelation method available in TMVA.

Correlations in % among the BDT input variables used for the muon final state in signal events of the 2J1T category after application of the decorrelation method available in TMVA.

Correlations in % among the BDT input variables used for the muon final state in signal events of the 2J1T category after application of the decorrelation method available in TMVA.

Correlations in % among the BDT input variables used for the muon final state in background events of the 2J1T category before application of the decorrelation method available in TMVA.

Correlations in % among the BDT input variables used for the muon final state in background events of the 2J1T category before application of the decorrelation method available in TMVA.

Correlations in % among the BDT input variables used for the muon final state in background events of the 2J1T category after application of the decorrelation method available in TMVA.

Correlations in % among the BDT input variables used for the muon final state in background events of the 2J1T category after application of the decorrelation method available in TMVA.

Correlations in % among the BDT input variables used for the electron final state in signal events of the 2J1T category before application of the decorrelation method available in TMVA.

Correlations in % among the BDT input variables used for the electron final state in signal events of the 2J1T category before application of the decorrelation method available in TMVA.

Correlations in % among the BDT input variables used for the electron final state in signal events of the 2J1T category after application of the decorrelation method available in TMVA.

Correlations in % among the BDT input variables used for the electron final state in signal events of the 2J1T category after application of the decorrelation method available in TMVA.

Correlations in % among the BDT input variables used for the electron final state in background events of the 2J1T category before application of the decorrelation method available in TMVA.

Correlations in % among the BDT input variables used for the electron final state in background events of the 2J1T category before application of the decorrelation method available in TMVA.

Correlations in % among the BDT input variables in background events for the elecron final state in the 2J1T category after decorrelation.

Correlations in % among the BDT input variables in background events for the elecron final state in the 2J1T category after decorrelation.

Correlations in % among the parameter of interest and the nuisance parameters corresponding to the fit to data for the $\ell^{\pm}$ final state in the 2J1T event category.

Correlations in % among the parameter of interest and the nuisance parameters corresponding to the fit to data for the $\ell^{\pm}$ final state in the 2J1T event category.

Data from Figure 32 – STEREO exclusion and exclusion sensitivity contours at 90% C.L. for 179 days reactor-on (phase-I+II). A full graphical presentation can be downloaded at "Resources" for reference.

Data from Figures 33 and 34 – STEREO exclusion and exclusion sensitivity contours at 95% C.L. for 179 days reactor-on (phase-I+II) using the two-dimensional method. A graphical presentation can be downloaded at "Resources" for reference.

Data from Figures 33 and 34 – STEREO exclusion and exclusion sensitivity contours at 95% C.L. for 179 days reactor-on (phase-I+II) using the two-dimensional method. A graphical presentation can be downloaded at "Resources" for reference.

$\Delta \chi^2$ map of phase-I+II data calculated by a raster-scan method. To be used in combination with the $\Delta \chi^2_{\text{crit},x}$ values to generate exclusion contours at $x$% C.L. for phase-I+II data. Additional explanations are given there.

Data from Figure 34 – STEREO exclusion and exclusion sensitivity contours at 95% C.L. for 179 days reactor-on (phase-I+II) using the raster-scan method. A graphical presentation can be downloaded at "Resources" for reference.

Data from Figure 34 – STEREO exclusion and exclusion sensitivity contours at 95% C.L. for 179 days reactor-on (phase-I+II) using the raster-scan method. A graphical presentation can be downloaded at "Resources" for reference.

The $\Delta \chi^2_{\text{crit},x}$ map accounts for the fact that the $\Delta \chi^2$ values of the oscillation fit do not follow a $\chi^2$ distribution with 2 degrees of freedom. Therefore, the $\Delta \chi^2_{\text{crit},x}$ value for x% C.L. of each point $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$ in the parameter space, $\Delta \chi^2_{\text{crit},x}(\sin^2(2\theta_{ee}), \Delta m^2_{41})$, is determined from the $\Delta \chi^2$ obtained in pseudo-experiments at that point, such that $\Delta \chi^2 \leq \Delta \chi^2_{\text{crit},x}$, for $x$% of the pseudo-experiments. Applying these $\Delta \chi^2_{\text{crit},x}$ values to the $\Delta \chi^2$ map obtained with the data, $x$% C.L. exclusion contours are obtained. The point $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$ is excluded by the data at $x$% C.L. if $\Delta \chi^2(\sin^2(2\theta_{ee}), \Delta m^2_{41}) > \Delta \chi^2_{\text{crit},x}(\sin^2(2\theta_{ee}), \Delta m^2_{41})$. In order to obtain the $\Delta \chi^2_{\text{crit},x}$ map, $10^4$ pseudo-experiments were generated for each point $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$ in the parameter space, taking into account all statistical and systematic uncertainties. The $\Delta \chi^2$ value of a pseudo-experiment is calculated by subtracting the $\chi^2$ value of the best-fit in the parameter space from the $\chi^2$ value of the fit at the $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$ used in the generation of the pseudo-dataset, where all nuisance parameters are free within their pull terms. When combining the exclusion contours with other experimental data, special care should be exercised. The assumption of a standard $\chi^2$ law instead of the provided $\Delta \chi^2_{\text{crit},x}$ values derived from non-standard $\chi^2$ distributions leads to slightly modified contours. In addition, the contours were derived using a raster-scan in several fixed values of $\Delta m_{41}^2$. While this method is particularly suited to derive exclusion contours, it cannot be used to calculate allowed confidence regions for $\Delta m_{41}^2$ and consequently two-dimensional allowed confidence regions. This is because $\Delta \chi^2$ values are not reflecting the likelihood of individual $\Delta m_{41}^2$ values. Thus, a direct comparison of $\Delta \chi^2$ values across different $\Delta m_{41}^2$ values is not possible in a statistically meaningful way. When generating the exclusion contours with the aforementioned procedure, spurious exclusion regions at low values of $\sin^2(2\theta_{ee})$ can be encountered for some values of $\Delta m^2_{41}$. These should be ignored and are owed to the raster-scan procedure used to generate the maps.

Data from Figure 34 – STEREO exclusion and exclusion sensitivity contours at 95% C.L. for 179 days reactor-on (phase-I+II) using the CLs method. A graphical presentation can be downloaded at "Resources" for reference.

Data from Figure 34 – STEREO exclusion and exclusion sensitivity contours at 95% C.L. for 179 days reactor-on (phase-I+II) using the CLs method. A graphical presentation can be downloaded at "Resources" for reference.

$\Delta \chi^2$ map of phase-I+II data calculated by the two-dimensional method. To be used in combination with the $\Delta \chi^2_{\text{crit},x}$ values of the two-dimensional method to generate exclusion contours at $x$% C.L. for phase-I+II data. Additional explanations are given there.

$\Delta \chi^2$ map of phase-I+II data calculated by the two-dimensional method. To be used in combination with the $\Delta \chi^2_{\text{crit},x}$ values of the two-dimensional method to generate exclusion contours at $x$% C.L. for phase-I+II data. Additional explanations are given there.

The $\Delta \chi^2_{\text{crit},x}$ map accounts for the fact that the $\Delta \chi^2$ values of the oscillation fit do not follow a $\chi^2$ distribution with 2 degrees of freedom. Therefore, the $\Delta \chi^2_{\text{crit},x}$ value for x% C.L. of each point $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$ in the parameter space, $\Delta \chi^2_{\text{crit},x}(\sin^2(2\theta_{ee}), \Delta m^2_{41})$, is determined from the $\Delta \chi^2$ obtained in pseudo-experiments at that point, such that $\Delta \chi^2 \leq \Delta \chi^2_{\text{crit},x}$, for $x$% of the pseudo-experiments. Applying these $\Delta \chi^2_{\text{crit},x}$ values to the $\Delta \chi^2$ map obtained with the data, $x$% C.L. exclusion contours are obtained. The point $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$ is excluded by the data at $x$% C.L. if $\Delta \chi^2(\sin^2(2\theta_{ee}), \Delta m^2_{41}) > \Delta \chi^2_{\text{crit},x}(\sin^2(2\theta_{ee}), \Delta m^2_{41})$. In order to obtain the $\Delta \chi^2_{\text{crit},x}$ map, $10^4$ pseudo-experiments were generated for each point $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$ in the parameter space, taking into account all statistical and systematic uncertainties. The $\Delta \chi^2$ value of a pseudo-experiment is calculated by subtracting the $\chi^2$ value of the best-fit in the parameter space from the $\chi^2$ value of the fit at the $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$ used in the generation of the pseudo-dataset, where all nuisance parameters are free within their pull terms. When combining the exclusion contours with other experimental data, special care should be exercised. The assumption of a standard $\chi^2$ law instead of the provided $\Delta \chi^2_{\text{crit},x}$ values derived from non-standard $\chi^2$ distributions leads to modified contours. In addition, for the interpretation of derived allowed confidence regions, one should always consider additional available information, in particular rate information. This is especially advisable considering the expected distribution of best-fit points in a prediction-free shape-only analysis, as discussed in the main publication.

The $\Delta \chi^2_{\text{crit},x}$ map accounts for the fact that the $\Delta \chi^2$ values of the oscillation fit do not follow a $\chi^2$ distribution with 2 degrees of freedom. Therefore, the $\Delta \chi^2_{\text{crit},x}$ value for x% C.L. of each point $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$ in the parameter space, $\Delta \chi^2_{\text{crit},x}(\sin^2(2\theta_{ee}), \Delta m^2_{41})$, is determined from the $\Delta \chi^2$ obtained in pseudo-experiments at that point, such that $\Delta \chi^2 \leq \Delta \chi^2_{\text{crit},x}$, for $x$% of the pseudo-experiments. Applying these $\Delta \chi^2_{\text{crit},x}$ values to the $\Delta \chi^2$ map obtained with the data, $x$% C.L. exclusion contours are obtained. The point $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$ is excluded by the data at $x$% C.L. if $\Delta \chi^2(\sin^2(2\theta_{ee}), \Delta m^2_{41}) > \Delta \chi^2_{\text{crit},x}(\sin^2(2\theta_{ee}), \Delta m^2_{41})$. In order to obtain the $\Delta \chi^2_{\text{crit},x}$ map, $10^4$ pseudo-experiments were generated for each point $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$ in the parameter space, taking into account all statistical and systematic uncertainties. The $\Delta \chi^2$ value of a pseudo-experiment is calculated by subtracting the $\chi^2$ value of the best-fit in the parameter space from the $\chi^2$ value of the fit at the $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$ used in the generation of the pseudo-dataset, where all nuisance parameters are free within their pull terms. When combining the exclusion contours with other experimental data, special care should be exercised. The assumption of a standard $\chi^2$ law instead of the provided $\Delta \chi^2_{\text{crit},x}$ values derived from non-standard $\chi^2$ distributions leads to modified contours. In addition, for the interpretation of derived allowed confidence regions, one should always consider additional available information, in particular rate information. This is especially advisable considering the expected distribution of best-fit points in a prediction-free shape-only analysis, as discussed in the main publication.

$\Delta \chi^2$ map of phase-I+II data calculated by the raster-scan method. To be used in combination with the $\Delta \chi^2_{\text{crit},x}$ values of the raster-scan method to generate exclusion contours at $x$% C.L. for phase-I+II data. Additional explanations are given there.

$\Delta \chi^2$ map of phase-I+II data calculated by the raster-scan method. To be used in combination with the $\Delta \chi^2_{\text{crit},x}$ values of the raster-scan method to generate exclusion contours at $x$% C.L. for phase-I+II data. Additional explanations are given there.

The $\Delta \chi^2_{\text{crit},x}$ and $\Delta \chi^2$ values in the raster-scan method serve the same purpose as in the two-dimensional method. The explanations given there also apply here, with the following changes: Instead of the two free oscillation parameters $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$, the raster-scan method fits only $\sin^2(2\theta_{ee})$ as free oscillation parameter while repeating the fit for several fixed values of $\Delta m_{41}^2$. While this method is particularly suited to derive exclusion contours, it cannot be used to calculate allowed confidence regions for $\Delta m_{41}^2$ and consequently two-dimensional allowed confidence regions. This is because $\Delta \chi^2$ values are not reflecting the likelihood of individual $\Delta m_{41}^2$ values. Thus, a direct comparison of $\Delta \chi^2$ values across different $\Delta m_{41}^2$ values is not possible in a statistically meaningful way. Moreover, when generating the exclusion contours with the aforementioned procedure, spurious exclusion regions at low values of $\sin^2(2\theta_{ee})$ can be encountered for some values of $\Delta m^2_{41}$. These should be ignored and are owed to the raster-scan procedure used to generate the maps.

The $\Delta \chi^2_{\text{crit},x}$ and $\Delta \chi^2$ values in the raster-scan method serve the same purpose as in the two-dimensional method. The explanations given there also apply here, with the following changes: Instead of the two free oscillation parameters $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$, the raster-scan method fits only $\sin^2(2\theta_{ee})$ as free oscillation parameter while repeating the fit for several fixed values of $\Delta m_{41}^2$. While this method is particularly suited to derive exclusion contours, it cannot be used to calculate allowed confidence regions for $\Delta m_{41}^2$ and consequently two-dimensional allowed confidence regions. This is because $\Delta \chi^2$ values are not reflecting the likelihood of individual $\Delta m_{41}^2$ values. Thus, a direct comparison of $\Delta \chi^2$ values across different $\Delta m_{41}^2$ values is not possible in a statistically meaningful way. Moreover, when generating the exclusion contours with the aforementioned procedure, spurious exclusion regions at low values of $\sin^2(2\theta_{ee})$ can be encountered for some values of $\Delta m^2_{41}$. These should be ignored and are owed to the raster-scan procedure used to generate the maps.

$\Delta T$ map of phase-I+II data calculated by the CLs method. To be used in combination with the $\Delta T_{\text{crit},x}$ values of the CLs method to generate exclusion contours at $x$% C.L. for phase-I+II data. Additional explanations are given there.

$\Delta T$ map of phase-I+II data calculated by the CLs method. To be used in combination with the $\Delta T_{\text{crit},x}$ values of the CLs method to generate exclusion contours at $x$% C.L. for phase-I+II data. Additional explanations are given there.

The $\Delta T_{\text{crit},x}$ and $\Delta T$ values in the CLs method fulfil a similar purpose as the $\Delta \chi^2_{\text{crit},x}$ and $\Delta \chi^2$ values in the two-dimensional method. The explanations given there also apply here, with the following changes: The CLs method differs from the two-dimensional method as it normalises the confidence level of the oscillation-hypothesis to the confidence level of the null-hypothesis (i.e. no-oscillation-hypothesis), instead of the best-fit. The $\Delta T$ value of a dataset at the parameter space-point $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$ is computed by subtracting the $\chi^2$ value of the fit under the no-oscillation hypothesis from the $\chi^2$ value of the fit with the oscillation parameters fixed to $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$. With this definition, $\Delta T$ values can be positive or negative as opposed to $\Delta \chi^2$ values. Note that the values used to compare with are $-\Delta T$ and not $\Delta T$ directly. The CLs value of a parameter space-point $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$ is defined as $\text{CL}_\text{s}(\sin^2(2\theta_{ee}), \Delta m^2_{41}):=(1-p_1)/(1-p_0)$ where $(1-p_0)$ and $(1-p_1)$ are the confidence levels of the dataset, determined from the distributions of $-\Delta T$ created in pseudo-experiments assuming the oscillation parameters $[0,0]$ and $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$, respectively. The point $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$ is excluded by the data at $x$% C.L. if $\Delta T(\sin^2(2\theta_{ee}), \Delta m^2_{41}) < \Delta T_{\text{crit},x}(\sin^2(2\theta_{ee}), \Delta m^2_{41})$. Note the flipped sign with respect to the two-dimensional method and the raster-scan method. More information on the CLs method can be found at "Resources".

The $\Delta T_{\text{crit},x}$ and $\Delta T$ values in the CLs method fulfil a similar purpose as the $\Delta \chi^2_{\text{crit},x}$ and $\Delta \chi^2$ values in the two-dimensional method. The explanations given there also apply here, with the following changes: The CLs method differs from the two-dimensional method as it normalises the confidence level of the oscillation-hypothesis to the confidence level of the null-hypothesis (i.e. no-oscillation-hypothesis), instead of the best-fit. The $\Delta T$ value of a dataset at the parameter space-point $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$ is computed by subtracting the $\chi^2$ value of the fit under the no-oscillation hypothesis from the $\chi^2$ value of the fit with the oscillation parameters fixed to $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$. With this definition, $\Delta T$ values can be positive or negative as opposed to $\Delta \chi^2$ values. Note that the values used to compare with are $-\Delta T$ and not $\Delta T$ directly. The CLs value of a parameter space-point $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$ is defined as $\text{CL}_\text{s}(\sin^2(2\theta_{ee}), \Delta m^2_{41}):=(1-p_1)/(1-p_0)$ where $(1-p_0)$ and $(1-p_1)$ are the confidence levels of the dataset, determined from the distributions of $-\Delta T$ created in pseudo-experiments assuming the oscillation parameters $[0,0]$ and $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$, respectively. The point $[\sin^2(2\theta_{ee}), \Delta m^2_{41}]$ is excluded by the data at $x$% C.L. if $\Delta T(\sin^2(2\theta_{ee}), \Delta m^2_{41}) < \Delta T_{\text{crit},x}(\sin^2(2\theta_{ee}), \Delta m^2_{41})$. Note the flipped sign with respect to the two-dimensional method and the raster-scan method. More information on the CLs method can be found at "Resources".

Map of mean $\Delta \chi^2$ values derived from simulations of phase-I+II and calculated by the two-dimensional method. To be used analogously to the $\Delta \chi^2$ map of phase-I+II data in combination with the $\Delta \chi^2_{\text{crit},x}$ values of the two-dimensional method to generate sensitivity contours at $x$% C.L. for phase-I+II. Additional explanations are given there.

Data from Figure 3b on charged pion production. Data correspond to charged particle production, corrected to the contribution from (PI+ + PI-)/2.

Data from Figure 3b on charged pion production. Data correspond to charged particle production, corrected to the contribution from (PI+ + PI-)/2.

Data from Figure 3c on charged pion production. Data correspond to charged particle production, corrected to the contribution from (PI+ + PI-)/2.

Data from Figure 3d on charged pion production. Data correspond to charged particle production, corrected to the contribution from (PI+ + PI-)/2.

Data from Figure 4a on forward jet production differential in x, low PT

Data from Figure 4a on forward jet production differential in x, high PT

Data from Figure 5a on forward jet production differential in delta(phi), low x

Data from Figure 5b on forward jet production differential in delta(phi), high x

Muons and jets from beauty photoproduction, energy fraction of the exchanged photon entering the hard subprocess

Muons and jets from beauty in deep-inelastic scattering, negative momentum transfer squared $Q^2$. Jets are reconstructed in the Breit frame of reference.

Muons and jets from beauty in deep-inelastic scattering, Bjorken-x. Jets are reconstructed in the Breit frame of reference.

Muons and jets from beauty in deep-inelastic scattering, muon pseudorapidity. Jets are reconstructed in the Breit frame of reference.

Muons and jets from beauty in deep-inelastic scattering, muon transverse momentum. Jets are reconstructed in the Breit frame of reference.

Muons and jets from beauty in deep-inelastic scattering, leading jet transverse momentum. Jets are reconstructed in the Breit frame of reference.

Muons and jets from beauty in photoproduction and deep-inelastic scattering. in photoproduction, two jets are required, with transverse momentum above 7 and 6 GeV, respectively. In Deep-inelastic scattering, jets are reconstructed in the Breit frame of reference, and only one jet is required, with transverse momentum above 6 GeV.

The distribution of Top jet $ au_{3}/ au_{2}$ for the simulation of backgrounds and a 2400 GeV (left-handed) b* signal. The area of the total background contribution and area of the signal component are separately normalized to unity. All analysis selections are applied with the exception of the variable being plotted.

The distribution of W jet $ au_{2}/ au_{1}$ for the simulation of backgrounds and a 2400 GeV (left-handed) b* signal. The area of the total background contribution and area of the signal component are separately normalized to unity. All analysis selections are applied with the exception of the variable being plotted.

The distribution of m_{W} for the simulation of backgrounds and a 2400 GeV (left-handed) b* signal. The area of the total background contribution and area of the signal component are separately normalized to unity. All analysis selections are applied with the exception of the variable being plotted.

The distribution of Top jet DeepCSV score for the simulation of backgrounds and a 2400 GeV (left-handed) b* signal. The area of the total background contribution and area of the signal component are separately normalized to unity. All analysis selections are applied with the exception of the variable being plotted.

The distribution of m_{t} for the simulation of backgrounds and a 2400 GeV (left-handed) b* signal. The area of the total background contribution and area of the signal component are separately normalized to unity. All analysis selections are applied with the exception of the variable being plotted.

Distributions of $m_{tW}$ in the signal region for an interval of $m_{t}$ (65-105 GeV).

Distributions of $m_{tW}$ in the signal region for an interval of $m_{t}$ (105-225 GeV).

Distributions of $m_{tW}$ in the signal region for an interval of $m_{t}$ (225-285 GeV).

Distributions of $m_{tW}$ in themultijet control region of the signal region for an interval of $m_{t}$ (65-105 GeV).

Distributions of $m_{tW}$ in themultijet control region of the signal region for an interval of $m_{t}$ (105-225 GeV).

Distributions of $m_{tW}$ in themultijet control region of the signal region for an interval of $m_{t}$ (225-285 GeV).

Distributions of $m_{t}$ in the ttbar measurement region for an interval of $m_{tar{t}}$ (1200-1300 GeV).

Distributions of $m_{t}$ in the ttbar measurement region for an interval of $m_{tar{t}}$ (1300-1800 GeV).

Distributions of $m_{t}$ in the ttbar measurement region for an interval of $m_{tar{t}}$ (1800-3000 GeV).

Distributions of $m_{t}$ in themultijet control region of the ttbar measurement region for an interval of $m_{tar{t}}$ (1200-1300 GeV).

Distributions of $m_{t}$ in themultijet control region of the ttbar measurement region for an interval of $m_{tar{t}}$ (1300-1800 GeV).

Distributions of $m_{t}$ in themultijet control region of the ttbar measurement region for an interval of $m_{tar{t}}$ (1800-3000 GeV).

The ratio between the observed data and the simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $p_T^{miss}$ after scaling the simulation to match the total yield in data.

Comparison between data and simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_t$ after scaling the simulation to match the total yield in data. The hatched region indicates the total shape uncertainty in the simulation.

The ratio between the observed data and the simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_t$ after scaling the simulation to match the total yield in data.

Comparison between data and simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_W$ after scaling the simulation to match the total yield in data. The hatched region indicates the total shape uncertainty in the simulation.

The ratio between the observed data and the simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_W$ after scaling the simulation to match the total yield in data.

Comparison between data and simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_{\text{res}}$ after scaling the simulation to match the total yield in data. The hatched region indicates the total shape uncertainty in the simulation.

The ratio between the observed data and the simulation in the high $\Delta$m portion of the $\ell+\text{jets}$ control region as a function of $N_{\text{res}}$ after scaling the simulation to match the total yield in data.

Observed event yields in data (black points) and predicted SM background (filled histograms) for the low $\Delta$m search bins 0--52. The signal models are denoted in the legend with the masses in GeV of the SUSY particles in parentheses: $(m_{\tilde{t}}, m_{\tilde{\chi}^0_1})$ or $(m_{\tilde{g}}, m_{\tilde{\chi}^0_1})$ for the T2 or T1 signal models, respectively. The hatched bands correspond to the total uncertainty in the background prediction. The (unstacked) distributions for two example signal models are also shown.

The ratio of the data to the total background prediction for the low $\Delta$m search bins 0--52. The hatched bands correspond to the total uncertainty in the background prediction.

Observed event yields in data (black points) and predicted SM background (filled histograms) for the high $\Delta$m search bins 53--104. The signal models are denoted in the legend with the masses in GeV of the SUSY particles in parentheses: $(m_{\tilde{t}}, m_{\tilde{\chi}^0_1})$ or $(m_{\tilde{g}}, m_{\tilde{\chi}^0_1})$ for the T2 or T1 signal models, respectively. The hatched bands correspond to the total uncertainty in the background prediction. The (unstacked) distributions for two example signal models are also shown.

The ratio of the data to the total background prediction for the high $\Delta$m search bins 53--104. The hatched bands correspond to the total uncertainty in the background prediction.

Observed event yields in data (black points) and predicted SM background (filled histograms) for the high $\Delta$m search bins 105--152 with ${N_b = 2}$. The signal models are denoted in the legend with the masses in GeV of the SUSY particles in parentheses: $(m_{\tilde{t}}, m_{\tilde{\chi}^0_1})$ or $(m_{\tilde{g}}, m_{\tilde{\chi}^0_1})$ for the T2 or T1 signal models, respectively. The hatched bands correspond to the total uncertainty in the background prediction. The (unstacked) distributions for two example signal models are also shown.

The ratio of the data to the total background prediction for the high $\Delta$m search bins 105--152 with ${N_b = 2}$. The hatched bands correspond to the total uncertainty in the background prediction.

Observed event yields in data (black points) and predicted SM background (filled histograms) for the high $\Delta$m search bins 153--182 with ${N_b \geq 3}$. The signal models are denoted in the legend with the masses in GeV of the SUSY particles in parentheses: $(m_{\tilde{t}}, m_{\tilde{\chi}^0_1})$ or $(m_{\tilde{g}}, m_{\tilde{\chi}^0_1})$ for the T2 or T1 signal models, respectively. The hatched bands correspond to the total uncertainty in the background prediction. The (unstacked) distributions for two example signal models are also shown.

The ratio of the data to the total background prediction for the high $\Delta$m search bins 153--182 with ${N_b \geq 3}$. The hatched bands correspond to the total uncertainty in the background prediction.

The observed 95% CL upper limit on the production cross section of the T2tt simplified model as a function of the top squark and LSP masses. No interpretation is provided for signal models for which ${|{m_{\tilde{t}} - m_{\tilde{\chi}^0_1} - m_t}| < 25 GeV}$ and ${m_{\tilde{t}} < 275 GeV}$ as described in the text.

The expected 95% CL upper limit on the production cross section of the T2tt simplified model as a function of the top squark and LSP masses. No interpretation is provided for signal models for which ${|{m_{\tilde{t}} - m_{\tilde{\chi}^0_1} - m_t}| < 25 GeV}$ and ${m_{\tilde{t}} < 275 GeV}$ as described in the text.

The observed exclusion contour of the T2tt simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$). No interpretation is provided for signal models for which ${|{m_{\tilde{t}} - m_{\tilde{\chi}^0_1} - m_t}| < 25 GeV}$ and ${m_{\tilde{t}} < 275 GeV}$ as described in the text.

The mean expected exclusion contour of the T2tt simplified model and the region containing 68 and 95\% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis. No interpretation is provided for signal models for which ${|{m_{\tilde{t}} - m_{\tilde{\chi}^0_1} - m_t}| < 25 GeV}$ and ${m_{\tilde{t}} < 275 GeV}$ as described in the text.

The observed 95% CL upper limit on the production cross section of the T2bW simplified model as a function of the top squark and LSP masses.

The expected 95% CL upper limit on the production cross section of the T2bW simplified model as a function of the top squark and LSP masses.

The observed exclusion contour of the T2bW simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).

The mean expected exclusion contour of the T2bW simplified model and the region containing 68 and 95\% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.

The observed 95% CL upper limit on the production cross section of the T2tb simplified model as a function of the top squark and LSP masses.

The expected 95% CL upper limit on the production cross section of the T2tb simplified model as a function of the top squark and LSP masses.

The observed exclusion contour of the T2tb simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).

The mean expected exclusion contour of the T2tb simplified model and the region containing 68 and 95\% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.

The observed 95% CL upper limit on the production cross section of the T2ttC simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.

The expected 95% CL upper limit on the production cross section of the T2ttC simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.

The observed exclusion contour of the T2ttC simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).

The mean expected exclusion contour of the T2ttC simplified model and the region containing 68\% ($\pm 1\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.

The observed 95% CL upper limit on the production cross section of the T2bWC simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.

The expected 95% CL upper limit on the production cross section of the T2bWC simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.

The observed exclusion contour of the T2bWC simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).

The mean expected exclusion contour of the T2bWC simplified model and the region containing 68\% ($\pm 1\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.

The observed 95% CL upper limit on the production cross section of the T2cc simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.

The expected 95% CL upper limit on the production cross section of the T2cc simplified model as a function of the top squark mass and the difference between the top squark and LSP masses.

The observed exclusion contour of the T2cc simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).

The mean expected exclusion contour of the T2cc simplified model and the region containing 68\% ($\pm 1\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.

The observed 95% CL upper limit on the production cross section of the T1tttt simplified model as a function of the gluino and LSP masses.

The expected 95% CL upper limit on the production cross section of the T1tttt simplified model as a function of the gluino and LSP masses.

The observed exclusion contour of the T1tttt simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).

The mean expected exclusion contour of the T1tttt simplified model and the region containing 68 and 95\% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.

The observed 95% CL upper limit on the production cross section of the T1ttbb simplified model as a function of the gluino and LSP masses.

The expected 95% CL upper limit on the production cross section of the T1ttbb simplified model as a function of the gluino and LSP masses.

The observed exclusion contour of the T1ttbb simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$).

The mean expected exclusion contour of the T1ttbb simplified model and the region containing 68 and 95\% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis.

The observed 95% CL upper limit on the production cross section of the T5ttcc simplified model as a function of the gluino and LSP masses. The upper limits do not take into account contributions from direct top squark pair production; however, its effect is small for $m_{\tilde{\chi}^0_1} > 600 GeV$, which corresponds to the phase space beyond the exclusions based on direct top squark pair production. The excluded regions based on direct top squark pair production from this search and earlier searches are indicated by the hatched areas.

The expected 95% CL upper limit on the production cross section of the T5ttcc simplified model as a function of the gluino and LSP masses. The uppser limits do not take into account contributions from direct top squark pair production; however, its effect is small for $m_{\tilde{\chi}^0_1} > 600 GeV$, which corresponds to the phase space beyond the exclusions based on direct top squark pair production. The excluded regions based on direct top squark pair production from this search and earlier searches are indicated by the hatched areas.

The observed exclusion contour of the T5ttcc simplified model with respect to approximate NNLO+NNLL signal cross sections and the change in this contour due to variation of these cross sections within their theoretical uncertainties ($\sigma_{\text{theory}}$). The expected and observed upper limits do not take into account contributions from direct top squark pair production; however, its effect is small for $m_{\tilde{\chi}^0_1} > 600 GeV$, which corresponds to the phase space beyond the exclusions based on direct top squark pair production. The excluded regions based on direct top squark pair production from this search and earlier searches are indicated by the hatched areas.

The mean expected exclusion contour of the T5ttcc simplified model and the region containing 68% and 95% ($\pm 1$ and $2\,\sigma_{\text{experiment}}$) of the distribution of expected exclusion limits under the background-only hypothesis. The expected and observed upper limits do not take into account contributions from direct top squark pair production; however, its effect is small for $m_{\tilde{\chi}^0_1} > 600 GeV$, which corresponds to the phase space beyond the exclusions based on direct top squark pair production. The excluded regions based on direct top squark pair production from this search and earlier searches are indicated by the hatched areas.

The FSR correction applied as a function of $\phi ^ * _ \eta$ for electrons.

The FSR correction applied as a function of the boson transverse momentum.

The measured differential cross-sections as a function of the boson rapidity for muons. The first uncertainty is due to the size of the dataset, the second is due to experimental systematic uncertainties, and the third is due to the luminosity.

The measured differential cross-sections as a function of the boson rapidity for electrons. The first uncertainty is due to the size of the dataset, the second is due to experimental systematic uncertainties, and the third is due to the luminosity.

The measured differential cross-sections as a function of $\phi ^ * _ \eta$ for muons. The first uncertainty is due to the size of the dataset, the second is due to experimental systematic uncertainties, and the third is due to the luminosity.

The measured differential cross-sections as a function of $\phi ^ * _ \eta$ for electrons. The first uncertainty is due to the size of the dataset, the second is due to experimental systematic uncertainties, and the third is due to the luminosity.

The measured differential cross-sections as a function of pT . The first uncertainty is due to the size of the dataset, the second is due to experimental systematic uncertainties, and the third is due to the luminosity.

The correlation matrix for the differential cross-section measurement as a function of Z boson rapidity, for the dimuon final state, excluding the luminosity uncertainty, which is fully correlated between bins.

The correlation matrix for the differential cross-section measurements as a function of the Z boson rapidity, for the dielectron final state, excluding the luminosity uncertainty, which is fully correlated between bins.

The correlation matrix for the differential cross-section measurement as a function of $\phi ^ * _ \eta$ , for the dimuon final state, excluding the luminosity uncertainty, which is fully correlated between bins.

The correlation matrix for the differential cross-section measurements as a function of $\phi ^ * _ \eta$ , for the dielectron final state, excluding the luminosity uncertainty, which is fully correlated between bins.

The correlation matrix for the differential cross-section measurements as a function of boson pT , for the dimuon final state, excluding the luminosity uncertainty, which is fully correlated between bins.