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We present a method that extends the capabilities of the PINpointing Orbit-Crossing Collapsed HIerarchical Objects (PINOCCHIO) code, allowing it to generate accurate dark matter halo mock catalogues in cosmological models where the linear growth factor and the growth rate depend on scale. Such cosmologies comprise, among others, models with massive neutrinos and some classes of modified gravity theories. We validate the code by comparing the halo properties from PINOCCHIO against N-body simulations, focusing on cosmologies with massive neutrinos: $ uLambda$CDM. We analyse the halo mass function, halo two-point correlation function, halo power spectrum and the moments of the halo density field, showing that PINOCCHIO reproduces the results from simulations with the same level of precision as the original code ($sim5-10%$). We demonstrate that the abundance of halos in cosmologies with massless and massive neutrinos from PINOCCHIO matches very well the outcome of simulations, and point out that PINOCCHIO can reproduce the $Omega_ u-sigma_8$ degeneracy that affects the halo mass function. We show that the clustering properties of the halos from PINOCCHIO matches accurately those from simulations both in real and redshift-space, in the latter case up to $k=0.3~h~{rm Mpc}^{-1}$. We finally point out that the first moments of the halo density field from simulations are precisely reproduced by PINOCCHIO. We emphasize that the computational time required by PINOCCHIO to generate mock halo catalogues is orders of magnitude lower than the one needed for N-body simulations. This makes this tool ideal for applications like covariance matrix studies within the standard $Lambda$CDM model but also in cosmologies with massive neutrinos or some modified gravity theories.
Interpreting observations of the Lyman-$alpha$ forest flux power spectrum requires interpolation between a small number of expensive simulations. We present a Gaussian process emulator modelling the 1D flux power spectrum as a function of the amplitu
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