tokens: model the angular mask. Different assumptions on the angular selection used to construct the random catalogue might impact the measurement at small scales ( s ≲ 10 h −1 Mpc), although the effect is within the current measurement uncertainties (see Appendix A.4 ). To minimise the impact of shot noise error due to the finite number of random objects, we construct our random catalogue to be 100 times larger than the XXL cluster sample. 3.4 Covariance matrix The covariance matrix, C i , j , which measures the variance and correlation between 2PCF bins, is defined as where the subscripts i and j run over the 2PCF bins, k refers to the 2PCF of the k th of N R catalogue realisations, and is the mean 2PCF of the N R samples. The normalisation factor, , which takes into account the fact that the N R realisations might not be independent, is , , and for the subsample, jackknife, and bootstrap methods, respectively ( Norberg et al. 2009 ). We assess the XXL 2PCF covariance matrix with the bootstrap method, using 1000 realisations obtained by resampling galaxy clusters from the original catalogue, with replacement. The impact of this choice is discussed in Appendix A.3 . 4 Cosmological analysis We perform a Bayesian statistical Markov chain Monte Carlo (MCMC) analysis of the 2PCF by sampling the posterior distribution of Ω M , the only free parameter in the assumed flat ΛCDM model considered. As we verified, the current clustering uncertainties do not allow us to consider more general cosmological scenarios, for example models with free dark energy equation of state parameters, by exploiting only the XXL cluster clustering. The joint cosmological analysis of XXL cluster number counts and clustering will be presented in a forthcoming paper. We consider the commonly used likelihood function, , defined as where is the inverse of the covariance matrix estimated from the data with the bootstrap method (Eq. 5 ), N is the number of comoving separation bins at which the 2PCF ( ξ ) is estimated, and the superscripts d and m stand for data and model. The likelihood is estimated at the mean pair separations in each bin (see Sect. 3.2 ). The 2PCF model in redshift space, ξ m ( s ), is computedas where ξ DM ( r , z ) is the real-space dark matter 2PCF, which we estimated by Fourier transforming the power spectrum, P DM ( k , z ), computed with the software CAMB ( Lewis Bridle 2002 ). Since the present cluster clustering analysis focuses at sufficiently large scales, the dark matter power spectrum can be safely computed in linear theory, , with marginal effects on our results. The model depends on two free quantities, fσ 8 and b eff σ 8 (since ), and on the reference background cosmology used both to convert angles and redshifts into distances and to estimate the real-space dark matter 2PCF (see e.g. Marulli et al. 2017 ). The geometric distortions caused by an incorrect assumption of the background cosmology are modelled by the α parameter, i.e. the ratio between the
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