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The cosmological constant $\Lambda$ and cold dark matter (CDM) model($\Lambda\text{CDM}$) is one of the pillars of modern cosmology and is widelyused as the de facto theoretical model by current and forthcoming surveys. Asthe nature of dark energy is very elusive, in order to avoid the problem ofmodel bias, here we present a novel null test at the perturbation level thatuses the growth of matter perturbation data in order to assess the concordancemodel. We analyze how accurate this null test can be reconstructed by usingdata from forthcoming surveys creating mock catalogs based on$\Lambda\text{CDM}$ and three models that display a different evolution of thematter perturbations, namely a dark energy model with constant equation ofstate $w$ ($w$CDM), the Hu \& Sawicki and designer $f(R)$ models, and wereconstruct them with a machine learning technique known as the GeneticAlgorithms. We show that with future LSST-like mock data our consistency testwill be able to rule out these viable cosmological models at more than5$\sigma$, help to check for tensions in the data and alleviate the existingtension of the amplitude of matter fluctuations$S_8=\sigma_8\left(\Omega_m/0.3\right)^{0.5}$.

Testing the $\Lambda$CDM paradigm with growth rate data and machine learning