A new emulator code for Cosmology: an application of machine learning algorithms to speed up cosmological analyses

In recent years, artificial intelligence has become omnipresent and is being used in various sectors, including cosmology. Machine learning algorithms are increasingly being used by scientific researchers to process and analyze the vast amounts of data provided by wide-field surveys and cosmological simulations. We have developed a machine learning-based code that speeds up cosmological analyses by emulating cosmological functions. The code is implemented in the public library CosmoBolognaLib and uses machine learning algorithms provided by the numerical library CosmoPower to build a neural network that imitates the output of the theoretical model of the two-point correlation function. We focused on emulating the model by varying four cosmological parameters and validated the emulator's accuracy by comparing its output to that of the original function. They applied their code to the analysis of cosmological simulations and performed a Bayesian analysis to derive the posterior probability distribution of the parameters, showing almost perfect correspondence with the original model. The innovative aspect of this work lies in the code's speed, which becomes thousands of times faster, reducing the total execution time from several tens of hours to a few seconds. Ww plan to improve the code by extending the emulation to other cosmological functions and expanding the emulator's range of validity to cover a wider parameter space. We believe that the potential applications of this methodology in the future are numerous and that its use will soon become a key element for future cosmological analyses conducted within the CosmoBolognaLib