butions of the nuisance parameters encoding the systematic uncertainties. The latter are Gaussian distributions for all sources, including statistical uncertainties arising from the limited number of preselected or opposite-sign **data** events in the estimation of the reducible background, or the limited number of simulated events. Correlations of a given nuisance parameter between the backgrounds and the signal are taken into account when relevant. Table 6 presents 95% CL upper limits on the number of BSM events, S 95 , that may contribute to the SRs. Normalising these by the integrated luminosity L of the **data** sample, they can be interpreted as upper limits on the visible BSM cross-section (σ vis ), defined as σ vis = σ prod × A × = S 95 /L, where σ prod is the production cross-section of an arbitrary BSM signal process, and A and are the corresponding fiducial acceptance and reconstruction efficiencies for the relevant SR. These limits are computed with asymptotic approximations of the probability distributions of the test statistic under the different hypotheses [84]. They were confirmed to be within 10% of an alternative computation based on pseudo-experiments. The probability of the observations being compatible with the SM-only hypothesis is quantified by the p-values displayed in table 6; the smallest, for