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A plethora of observational data obtained over the last couple of decades hasallowed cosmology to enter into a precision era and has led to the foundationof the standard cosmological constant and cold dark matter paradigm, known asthe $\Lambda$CDM model. Given the many possible extensions of this concordancemodel, we present here several novel consistency tests which could be used toprobe for deviations from $\Lambda$CDM. First, we derive a joint consistencytest for the spatial curvature $\Omega_{k,0}$ and the matter density$\Omega_\textrm{m,0}$ parameters, constructed using only the Hubble rate$H(z)$, which can be determined directly from observations. Second, we presenta new test of possible deviations from homogeneity using the combination of twodatasets, either the baryon acoustic oscillation (BAO) and $H(z)$ data or thetransversal and radial BAO data, while we also introduce two consistency testsfor $\Lambda$CDM which could be reconstructed via the transversal and radialBAO data. We then reconstruct the aforementioned tests using the currentlyavailable data in a model independent manner using a particular machinelearning approach, namely the Genetic Algorithms. Finally, we also report on a$\sim 4\sigma$ tension on the transition redshift as determined by the $H(z)$and radial BAO data.

Novel null tests for the spatial curvature and homogeneity of the Universe and their machine learning reconstructions