Expected and observed limits computed using asymptotic formulas as a function of the signal mass m_{G*} and k/Mpl for the spin-2 resonance search.

The expected and observed upper limits at 95\% CL on the fiducial cross-section times branching ratio to two photons of a spin-0 resonance as a function of its mass m_X. The decay width of the resonance is equal to 2% of m_X. For masses greater than 1000 GeV, pseudo-experiments are used to verify the expected and observed limits, and used in place of the asymptotic limit when differences are observed.

The expected and observed upper limits at 95\% CL on the fiducial cross-section times branching ratio to two photons of a spin-0 resonance as a function of its mass m_X. The decay width of the resonance is equal to 6% of m_X. For masses greater than 1000 GeV, pseudo-experiments are used to verify the expected and observed limits, and used in place of the asymptotic limit when differences are observed.

The expected and observed upper limits at 95\% CL on the fiducial cross-section times branching ratio to two photons of a spin-0 resonance as a function of its mass m_X. The decay width of the resonance is equal to 10% of m_X. For masses greater than 1000 GeV, pseudo-experiments are used to verify the expected and observed limits, and used in place of the asymptotic limit when differences are observed.

The expected and observed upper limits at 95\% CL on the production cross-section times branching ratio to two photons of the lightest KK graviton as a function of its mass for k/Mpl=0.01. For masses greater than 1000 GeV, pseudo-experiments are used to verify the expected and observed limits, and used in place of the asymptotic limit when differences are observed.

The expected and observed upper limits at 95\% CL on the production cross-section times branching ratio to two photons of the lightest KK graviton as a function of its mass for k/Mpl=0.05. For masses greater than 1000 GeV, pseudo-experiments are used to verify the expected and observed limits, and used in place of the asymptotic limit when differences are observed.

Diphoton invariant mass in the signal region using a binning based on the invariant mass resolution.

Diphoton invariant mass in the signal region binned in 1 GeV bins..

Effect of event selections on a NWA spin-0 and a spin-2 ($k/Mpl=0.01$) MC samples generated for m_X = 1 TeV and on the data. For the MC samples, the event yield is shown after applying event weights; 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. The cross-section times branching ratio for the spin-0 sample is normalized to the MadGraph NLO prediction of 112~fb and the cross-section times branching ratio for the graviton corresponds to the Pythia8 prediction of 12~fb.

Parameterization for the CX and AX factors as function of mX for a scalar particle, as obtained from the narrow-width MC signal samples.

Parameterization for the $C_{X}\cdot A_{X}$ factor as function of $m_{G^*}$ for the RS graviton, as obtained from k/MPl = 0.05 MC signal samples.

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

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

Sequential impact of each requirement on the number of events passing the selection for the high-$E_T$ analysis.

Sequential impact of each requirement on the number of events passing the selection for the low-$E_T$ analysis.

Application of the modified ABCD method to the final high-$E_T$ and low-$E_T$ selections.

The extrapolated signal efficiencies as a function of proper decay length of the $s$ for several simulated samples in the low-$E_T$ selections.

The extrapolated signal efficiencies as a function of proper decay length of the $s$ for several simulated samples in the high-$E_T$ selections.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 25 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 600 ~\mathrm{GeV}$ and $m_{s} = 150 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 25 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 600 ~\mathrm{GeV}$ and $m_{s} = 150 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 5 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 8 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 15 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 40 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 55 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 200 ~\mathrm{GeV}$ and $m_{s} = 8 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 200 ~\mathrm{GeV}$ and $m_{s} = 25 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 200 ~\mathrm{GeV}$ and $m_{s} = 50 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 400 ~\mathrm{GeV}$ and $m_{s} = 50 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 400 ~\mathrm{GeV}$ and $m_{s} = 100 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 600 ~\mathrm{GeV}$ and $m_{s} = 50 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 1000 ~\mathrm{GeV}$ and $m_{s} = 50 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 1000 ~\mathrm{GeV}$ and $m_{s} = 150 ~\mathrm{GeV}$.

The observed limits, expected limits and $\pm 1 \sigma$ and $\pm 2 \sigma$ bands for a model with $m_{\phi} = 1000 ~\mathrm{GeV}$ and $m_{s} = 400 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 5 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 8 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 15 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 125 ~\mathrm{GeV}$ and $m_{s} = 40 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 200 ~\mathrm{GeV}$ and $m_{s} = 8 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 200 ~\mathrm{GeV}$ and $m_{s} = 25 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 200 ~\mathrm{GeV}$ and $m_{s} = 50 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 400 ~\mathrm{GeV}$ and $m_{s} = 50 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 400 ~\mathrm{GeV}$ and $m_{s} = 100 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 600 ~\mathrm{GeV}$ and $m_{s} = 50 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 1000 ~\mathrm{GeV}$ and $m_{s} = 50 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 1000 ~\mathrm{GeV}$ and $m_{s} = 150 ~\mathrm{GeV}$.

Combined limits for the CalRatio and MS analyses, for a model with $m_{\phi} = 1000 ~\mathrm{GeV}$ and $m_{s} = 400 ~\mathrm{GeV}$.

Differential cross-section and uncertainty from each source, as a function of leading $p_{T}^j$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of leading $p_{T}^j$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of leading $p_{T}^j$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of leading $p_{T}^j$ for the dominant process in the extreme $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of subleading $p_{T}^j$ for the dominant process in the $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of subleading $p_{T}^j$ for the dominant process in the $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of subleading $p_{T}^j$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of subleading $p_{T}^j$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of subleading $p_{T}^j$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of subleading $p_{T}^j$ for the dominant process in the extreme $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_0,l)$ for the dominant process in the $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_0,l)$ for the dominant process in the $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_0,l)$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_0,l)$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_0,l)$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_0,l)$ for the dominant process in the extreme $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_1,l)$ for the dominant process in the $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_1,l)$ for the dominant process in the $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_1,l)$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_1,l)$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_1,l)$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $min\Delta\phi(j_1,l)$ for the dominant process in the extreme $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\eta_{jj}$ for the dominant process in the $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\eta_{jj}$ for the dominant process in the $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\eta_{jj}$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\eta_{jj}$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\eta_{jj}$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\eta_{jj}$ for the dominant process in the extreme $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{jj}$ for the dominant process in the $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{jj}$ for the dominant process in the $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{jj}$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{jj}$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{jj}$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{jj}$ for the dominant process in the extreme $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{ll}$ for the dominant process in the $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{ll}$ for the dominant process in the $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{ll}$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{ll}$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{ll}$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $\Delta\phi_{ll}$ for the dominant process in the extreme $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $m_{jj}$ for the dominant process in the $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $m_{jj}$ for the dominant process in the $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $m_{jj}$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $m_{jj}$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $m_{jj}$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $m_{jj}$ for the dominant process in the extreme $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $p_{T}^{ee}$ for the dominant process in the $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $p_{T}^{\mu\mu}$ for the dominant process in the $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $p_{T}^{e\mu}$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $p_{T}^{ee}$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $p_{T}^{\mu\mu}$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $p_{T}^{e\mu}$ for the dominant process in the extreme $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $H_{T}$ for the dominant process in the $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $H_{T}$ for the dominant process in the $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $H_{T}$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $H_{T}$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $H_{T}$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $H_{T}$ for the dominant process in the extreme $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $S_{T}$ for the dominant process in the $ee jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $S_{T}$ for the dominant process in the $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $S_{T}$ for the dominant process in the $e\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $S_{T}$ for the dominant process in the extreme $eejj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $S_{T}$ for the dominant process in the extreme $\mu\mu jj$ measurement region.

Differential cross-section and uncertainty from each source, as a function of $S_{T}$ for the dominant process in the extreme $e\mu jj$ measurement region.

Expected and observed 95% CL lower limits on first- and second-generation leptoquark masses for different values of $\beta$.

Event yields in the dimuon channel control regions with total uncertainties. The observed number of events is given in the first row. The background event numbers as obtained from the fit are shown together with the total uncertainties. The second row shows the total background expectation, the further rows show the breakdown into different background components.

Event yields in the dielectron channel control regions with total uncertainties. The observed number of events is given in the first row. The background event numbers as obtained from the fit are shown together with the total uncertainties. The second row shows the total background expectation, the further rows show the breakdown into different background components.

Distribution of $m_{LQ}^{min}$ in the training region for the BDT for the $ee jj$ and $\mu\mu jj$ channels. Data are shown together with predicted total background expectation.

Distribution of $m_{LQ}^{T}$ in the training region for the BDT for the $e\nu jj$ and $\mu\nu jj$ channels. Data are shown together with predicted total background expectation.