STEREO detection and selection efficiency for Phase-II and III. The efficiency is given in 22 antineutrino energy bins, bins 2-21 corresponding to the binning of the unfolded spectrum ranging 2.375-7.875 MeV, bin 1 integrates from 1.875 MeV to 2.375 MeV and bin 22 integrates from 7.875 MeV to 8.125 MeV.

STEREO Detector Response Matrix for Phase-II and III. The matrix is given as a 22x22 matrix, with 22 rows corresponding to the 22 bins of the prompt spectrum (1.625-7.125MeV) and 22 columns corresponding to 22 true antineutrino energy bins described with the efficiency vectors. Before use, the matrix should be normalized so that $\sum_j R_{ji} = \text{efficiency}_i$.

STEREO unfolded U-235 spectrum, given with 20 antineutrino energy bins (bins 1-19 are 250 keV wide, with centers ranging from 2.5 MeV to 7 MeV and bin 20 is 750 keV wide with center at 7.5 MeV). The prediction from Huber multiplied by the IBD cross section is also given.

Covariance matrix of the unfolded U-235 spectrum, given as a 20x20 matrix with 20 antineutrino energy bins (bins 1-19 are 250 keV wide, with centers ranging from 2.5 MeV to 7 MeV and bin 20 is 750 keV wide with center at 7.5 MeV).

Filter matrix associated with the Tikhonov-regularized unfolding. The filter matrix $A$ encodes all biases coming from the unfolding process itself. The matrix is given as a 22x22 matrix, bins 2-21 correspond to the 20 bins of the unfolded spectrum (2.375-7.875 MeV), bin 1 integrates from 1.875 MeV to 2.375 MeV, bin 22 integrates from 7.875 MeV to 8.125 MeV. It brings any model from true $E_\nu$ space to the filtered $E_\nu$ space where it can be compared to the unfolded spectrum $\Phi$, for instance with $\chi2 = (A \cdot \text{model} - \Phi)^T V_\Phi^{-1} (A\cdot \text{model} - \Phi)$. The unfolding process introduce very little biases so $A$ is close to diagonal.

STEREO IBD Spectrum in each cell for phase-II and phase-III. The spectra are given in nu/day with 11 500keV-wide measured-energy bins, ranging from 1.625MeV (lower edge of lowest bin) to 7.125 MeV (upper edge of highest bin), with statistical uncertainties. We also give the no-oscillation model after a common shape to all cells has been absorbed by the fitting procedure.

STEREO $\Delta\chi^2$ map (2D Feldman-Cousins method) obtained from STEREO data. $\Delta\chi^2$ is defined as $\Delta\chi^2 = \chi^2_\text{H0} - \chi^2_\text{min}$ with H0 the no-oscillation hypothesis. A $(\sin2\theta_{ee},\Delta m^2_{41})$ point is excluded at n%CL if $\Delta\chi^2 > \Delta\chi^2_{crit,n}$. To be used with the critical-$\Delta\chi^2$ map to obtain the 2D exclusion contour.

STEREO median $\Delta\chi^2$ map (2D Feldman-Cousins method) obtained from $10^4$ no-oscillation toys. $\Delta\chi^2$ is defined as $\Delta\chi^2 = \chi^2_\text{H0} - \chi^2_\text{min}$ with H0 the no-oscillation hypothesis. A $(\sin2\theta_{ee},\Delta m^2_{41})$ point is excluded at n%CL if $\Delta\chi^2 > \Delta\chi^2_{crit,n}$. To be used with the critical-$\Delta\chi^2$ map to obtain the 2D sensitivity contour.

STEREO $\Delta T$ map (CLs method) obtained from STEREO data. CLs is defined as $(1-p1)/(1-p0)$ with $p0$ [$p1$] the p-value from the $\Delta T$ p.d.f. obtained with toys following the no-oscillation $(0,0)$ [$(\sin 2\theta_{ee},\Delta m^2_{41})$] model. $\Delta T$ is defined as $\Delta T = \chi^2_\text{H1} - \chi^2_\text{H0}$ with H0 [H1] the no-oscillation hypothesis [the $(\sin 2\theta_{ee},\Delta m^2_{41})$ hypothesis]. A $(\sin 2\theta_{ee},\Delta m^2_{41})$ point is excluded at n%CL if $\Delta T > \Delta T_{crit,n}$. To be used with the critical $\Delta T$ map to obtain the CLs exclusion contour.

STEREO median $\Delta T$ map (CLs method) obtained from $5\times 10^3$ no-oscillation toys. CLs is defined as $(1-p1)/(1-p0)$ with $p0$ [$p1$] the p-value from the $\Delta T$ p.d.f. obtained with toys following the no-oscillation $(0,0)$ [$(\sin 2\theta_{ee},\Delta m^2_{41})$] model. $\Delta T$ is defined as $\Delta T = \chi^2_\text{H1} - \chi^2_\text{H0}$ with H0 [H1] the no-oscillation hypothesis [the $(\sin 2\theta_{ee},\Delta m^2_{41})$ hypothesis]. A $(\sin 2\theta_{ee},\Delta m^2_{41})$ point is excluded at n%CL if $\Delta T > \Delta T_{crit,n}$. To be used with the critical $\Delta T$ map to obtain the CLs sensitivity contour.

STEREO exclusion and sensitivity contours in the $(\sin 2\theta_{ee},\Delta m^2_{41})$ plane (2D Feldman-Cousins method).

STEREO exclusion and sensitivity contours in the $(\sin 2\theta_{ee},\Delta m^2_{41})$ plane (CLs method).

STEREO critical-$\Delta\chi^2$ map (2D Feldman-Cousins method) for 95%CL and 99%CL. A $(\sin2\theta_{ee},\Delta m^2_{41})$ point is excluded at n%CL if $\Delta\chi^2 > \Delta\chi^2_{crit,n}$. To be used with the $\Delta\chi^2$ maps obtained with STEREO data (exclusion) or with no-oscillation toys (sensitivity).

STEREO critical $\Delta T$ map (CLs method) for 95%CL and 99%CL. A $(\sin2\theta_{ee},\Delta m^2_{41})$ point is excluded at n%CLs if $\Delta T > \Delta T_{crit,n}$. To be used with the $\Delta T$ maps obtained with STEREO data (exclusion) or with no-oscillation toys (sensitivity).

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 6 – Selection efficiency as a function of $E_\nu$.

Spectrum prediction for ILL's High Flux Reactor, given in 50keV-wide $E_\nu$ bins (centers ranging from 1.8 to 10 MeV). Huber's $^{235}$U prediction in [2 MeV, 8 MeV] is taken from Phys. Rev. C 84 024617 (2011); exponential extrapolations are performed as described in Phys. Rev. Lett. 125 201801 (2020). Relative corrections from Off-equilibrium and Activation are included to obtain the total HFR's spectrum. The IBD cross section we used is based on Strumia-Vissani Phys. Lett. B, 564 42–54 (2003). The IBD yield is simply HFR's spectrum $\times$ IBD cross section. More details can be found in Section 5, where all notations are also introduced.

Data from Figure 14 - STEREO pure-$^{235}$U unfolded spectrum. Error bars are square-roots of diagonal coefficients of the covariance matrix, and include statistical and systematic uncertainties.

Covariance matrix of the unfolded $^{235}$U spectrum, including statistical and systematic uncertainties. It is denoted $V_\Phi$ in Section 8.

Expected and observed number of events with m_ll > 1300 GeV in the dielectron and dimuon channels. Yields given for different MS values correspond to the sum of signal and background events, with the signal obtained in the GRW formalism. All yields are normalized to the Z peak control region. The errors quoted originate from systematic uncertainties and limited MC statistics.

Expected and observed 95% C.L. lower limits on MS in the dielectron and dimuon channels, as well as for the combination of those channels without and with the diphoton channel in the GRW formalism. Separate results are provided for the different choices of flat priors.

Observed 95% C.L. lower limits on MS (in units of TeV), including systematic uncertainties, for ADD signal in the GRW, Hewett and HLZ formalisms with no K-factor applied to the signal. Separate results are provided for the different choices of flat priors.

Observed 95% C.L. lower limits on of TeV), including systematic uncertainties, for in the GRW, Hewett and HLZ formalisms with K-factors of 1.6 and 1.7 applied to the signal for the dilepton and diphoton channels, respectively. Separate results are provided for the different choices of flat priors.

Number of tracks in vertex vs invariant mass of vertex (DATA) The number of tracks in the vertex vs the invariant mass of the vertex, for the data, and for the MH signal sample respectively. All selection cuts are applied, with the exceptions of those on the variables in the plot (Ntrk and mass), and the offline muon requirements.

Number of tracks in vertex vs invariant mass of vertex (SIGNAL) The number of tracks in the vertex vs the invariant mass of the vertex, for the data, and for the MH signal sample respectively. All selection cuts are applied, with the exceptions of those on the variables in the plot (Ntrk and mass), and the offline muon requirements.

95% CL upper limit on sigma.BR^2 vs c*tau The observed 95% CL upper limits on the cross-section for squark production, multiplied by the branching fraction for both squarks to decay to long-lived neutralinos, which in turn decay to mu+jets, given that we observed zero events in the signal region.

Expected upper limit on sigma.BR^2 vs c*tau The expected 95% CL upper limits on the cross-section for squark production, multiplied by the branching fraction for both squarks to decay to long-lived neutralinos, which in turn decay to mu+jets.

The Rcp values as a function of jet PT for the four R values, 0.2, 0.3, 0.4 and 0.5 for the collision centrality in the range 20 - 30 %.

The Rcp values as a function of jet PT for the four R values, 0.2, 0.3, 0.4 and 0.5 for the collision centrality in the range 30 - 40 %.

The Rcp values as a function of jet PT for the four R values, 0.2, 0.3, 0.4 and 0.5 for the collision centrality in the range 40 - 50 %.

The Rcp values as a function of jet PT for the four R values, 0.2, 0.3, 0.4 and 0.5 for the collision centrality in the range 50 - 60 %.

The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 38.36 - 44.21 GeV.

The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 44.21 - 50.94 GeV.

The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 50.94 - 58.70 GeV.

The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 58.70 - 67.64 GeV.

The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 67.64 - 77.94 GeV.

The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 77.94 - 89.81 GeV.

The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 89.81 - 103.5 GeV.

The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 103.5 - 119.3 GeV.

The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 119.3 - 137.4 GeV.

The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 137.4 - 158.3 GeV.

The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 158.3 - 182.5 GeV.

The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 182.5 - 210.3 GeV.

The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 38.36 - 44.21 GeV.

The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 38.36 - 44.21 GeV.

The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 44.21 - 50.94 GeV.

The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 44.21 - 50.94 GeV.

The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 50.94 - 58.70 GeV.

The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 50.94 - 58.70 GeV.

The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 58.70 - 67.64 GeV.

The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 58.70 - 67.64 GeV.

The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 67.64 - 77.94 GeV.

The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 67.64 - 77.94 GeV.

The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 77.94 - 89.81 GeV.

The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 77.94 - 89.81 GeV.

The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 89.81 - 103.5 GeV.

The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 89.81 - 103.5 GeV.

The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 103.5 - 119.3 GeV.

The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 103.5 - 119.3 GeV.

The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 119.3 - 137.4 GeV.

The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 119.3 - 137.4 GeV.

The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 137.4 - 158.3 GeV.

The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 137.4 - 158.3 GeV.

The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 158.3 - 182.5 GeV.

The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 158.3 - 182.5 GeV.

The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 182.5 - 210.3 GeV.

The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 182.5 - 210.3 GeV.

The ratios of Rcp between R=0.3, 0.4 and 0.5 and R=0.2 jets as a function of the jet PT for the centrality range 0 - 10 %.

The ratios of Rcp between R=0.3, 0.4 and 0.5 and R=0.2 jets as a function of the jet PT for the centrality range 10 - 20 %.

The ratios of Rcp between R=0.3, 0.4 and 0.5 and R=0.2 jets as a function of the jet PT for the centrality range 20 - 30 %.

The ratios of Rcp between R=0.3, 0.4 and 0.5 and R=0.2 jets as a function of the jet PT for the centrality range 30 - 40 %.

The ratios of Rcp between R=0.3, 0.4 and 0.5 and R=0.2 jets as a function of the jet PT for the centrality range 40 - 50 %.

The ratios of Rcp between R=0.3, 0.4 and 0.5 and R=0.2 jets as a function of the jet PT for the centrality range 50 - 60 %.

The covariance matrix for statistcal correlations for R = 0.2 and centrality range 0 - 10 %.

The covariance matrix for statistcal correlations for R = 0.3 and centrality range 0 - 10 %.

The covariance matrix for statistcal correlations for R = 0.4 and centrality range 0 - 10 %.

The covariance matrix for statistcal correlations for R = 0.5 and centrality range 0 - 10 %.

The covariance matrix for statistcal correlations for R = 0.2 and centrality range 10 - 20 %.

The covariance matrix for statistcal correlations for R = 0.3 and centrality range 10 - 20 %.

The covariance matrix for statistcal correlations for R = 0.4 and centrality range 10 - 20 %.

The covariance matrix for statistcal correlations for R = 0.5 and centrality range 10 - 20 %.

The covariance matrix for statistcal correlations for R = 0.2 and centrality range 20 - 30 %.

The covariance matrix for statistcal correlations for R = 0.3 and centrality range 20 - 30 %.

The covariance matrix for statistcal correlations for R = 0.4 and centrality range 20 - 30 %.

The covariance matrix for statistcal correlations for R = 0.5 and centrality range 20 - 30 %.

The covariance matrix for statistcal correlations for R = 0.2 and centrality range 30 - 40 %.

The covariance matrix for statistcal correlations for R = 0.3 and centrality range 30 - 40 %.

The covariance matrix for statistcal correlations for R = 0.4 and centrality range 30 - 40 %.

The covariance matrix for statistcal correlations for R = 0.5 and centrality range 30 - 40 %.

The covariance matrix for statistcal correlations for R = 0.2 and centrality range 40 - 50 %.

The covariance matrix for statistcal correlations for R = 0.3 and centrality range 40 - 50 %.

The covariance matrix for statistcal correlations for R = 0.4 and centrality range 40 - 50 %.

The covariance matrix for statistcal correlations for R = 0.5 and centrality range 40 - 50 %.

The covariance matrix for statistcal correlations for R = 0.2 and centrality range 50 - 60 %.

The covariance matrix for statistcal correlations for R = 0.3 and centrality range 50 - 60 %.

The covariance matrix for statistcal correlations for R = 0.4 and centrality range 50 - 60 %.

The covariance matrix for statistcal correlations for R = 0.5 and centrality range 50 - 60 %.

The jet width distribution for R=1.0 jets in the full 2010 dataset corrected for pileup and corrected to the particle level.

The jet eccentricity distribution for R=0.6 and high mass (M>100 GEV) jets in the full 2010 dataset corrected for pileup and corrected to the particle level.

The jet eccentricity distribution for R=1.0 and high mass (M>100 GEV) jets in the full 2010 dataset corrected for pileup and corrected to the particle level.

The jet planarflow distribution for R=1.0 of high mass (130>M>210 GEV) jets in NPV=1 events corrected to the particle level.

The jet angularity distribution for R=0.6 jets corrected to the particle level.

The Acceptance, Efficiency and Acceptance x Efficiency for the tau+muon channel as a function of Lambda and Tan(Beta).

The Acceptance, Efficiency and Acceptance x Efficiency for the tau+electron channel as a function of Lambda and Tan(Beta).

Acceptance in the GLUINO-NEUTRALINO plane in the Gbb model for the 2 btags signal region with Meff > 700 GeV.

Acceptance in the GLUINO-NEUTRALINO plane in the Gbb model for the 1 btag signal region with Meff > 900 GeV.

Acceptance in the GLUINO-NEUTRALINO plane in the Gbb model for the 2 btags signal region with Meff > 900 GeV.

Efficiency in the GLUINO-NEUTRALINO plane in the Gbb model for the 1 btag signal region with Meff > 500 GeV.

Efficiency in the GLUINO-NEUTRALINO plane in the Gbb model for the 2 btags signal region with Meff > 500 GeV.

Efficiency in the GLUINO-NEUTRALINO plane in the Gbb model for the 1 btag signal region with Meff > 700 GeV.

Efficiency in the GLUINO-NEUTRALINO plane in the Gbb model for the 2 btags signal region with Meff > 700 GeV.

Efficiency in the GLUINO-NEUTRALINO plane in the Gbb model for the 1 btag signal region with Meff > 900 GeV.

Efficiency in the GLUINO-NEUTRALINO plane in the Gbb model for the 2 btags signal region with Meff > 900 GeV.

The GLUINO-NEUTRALINO plane in the Gbb model showning which of the six signal regions results in the best expected limit at each point. At each point the signal region shown is used to construct the final limit.

Acceptance in the GLUINO-NEUTRALINO plane in the Gtt model for the SR1-D signal regions.

Acceptance in the GLUINO-NEUTRALINO plane in the Gtt model for the SR1-E signal regions.

Efficiency in the GLUINO-NEUTRALINO plane in the Gtt model for the SR1-D signal regions.

Efficiency in the GLUINO-NEUTRALINO plane in the Gtt model for the SR1-E signal regions.

Observed limit Obs.

Expected limit Exp.

Expected limit +1sigma Expu1s.

Figure 7 Expd1s.

Figure 7 Obs.

Figure 7 Exp.

Figure 7 Expu1s.

Figure 7 Expd1s.

.

Observed limit Obs.

Expected limit Exp.

Expected limit +1sigma Expu1s.

Expected limit +1sigma Expd1s.

Figure 9 Obs.

Figure 9 Exp.

Figure 9 Expu1s.

Figure 9 Expd1s.

Figure 9.

Figure 10 Obs.

Figure 10 Exp.

Figure 10 Expu1s.

Figure 10 Expd1s.

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