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The recently launched NASA Transiting Exoplanet Survey Satellite (TESS)mission is going to collect lightcurves for a few hundred million of stars andwe expect to increase the number of pulsating stars to analyze compared to thefew thousand stars observed by the CoRoT, $\textit{Kepler}$ and K2 missions.However, most of the TESS targets have not yet been properly classified andcharacterized. In order to improve the analysis of the TESS data, it is crucialto determine the type of stellar pulsations in a timely manner. We propose anautomatic method to classify stars attending to their pulsation properties, inparticular, to identify solar-like pulsators among all TESS targets. It relieson the use of the global amount of power contained in the power spectrum(already known as the FliPer method) as a key parameter, along with theeffective temperature, to feed into a machine learning classifier. Our study,based on TESS simulated datasets, shows that we are able to classify pulsatorswith a $98\%$ accuracy.

FliPer: Classifying TESS pulsating stars