Rachel Zhang and collaborators tested whether machine learning can estimate properties of massive O-type stars from TESS light curves. Using spectroscopic data from the IACOB project, they compared two approaches: neural networks trained on simple “red noise” parameters versus convolutional networks trained on full periodograms. The latter performed much better, showing that light curves contain enough information to predict stellar temperatures and luminosities, a valuable tool for future large surveys.