ROSE (Red-giant Oscillations Spectra Estimator): A Modular Machine-Learning Framework for Automated Asteroseismic Characterisation. I. nu and nu from TESS

Space-based photometry from Kepler and TESS has delivered oscillation spectra for hundreds of thousands of red giants, and PLATO and the Roman Galactic Bulge Time-Domain Survey will add more. We present ROSE, a modular machine-learning framework for automated asteroseismic characterisation of red giants, and apply two of its modules to the frequency of maximum oscillation power, ν max, and the large frequency separation, Δν. Each module is a neural network bundled with its own preprocessing transform and output binning, so that each parameter is measured in the representation that displays it most effectively: ν max from the power-density spectrum, and Δν from the power spectrum of the power spectrum taken over a window centred on ν max. Both are trained only on synthetic spectra generated at a one-year baseline and both return probability distributions rather than point estimates. This enables us to retrieve asymmetric uncertainty and a reliability index. Validating against held-out synthetic spectra, Kepler spectra at the training resolution and TESS continuous-viewing-zone giants, we obtain robust dispersions of 1.4, 3.3 and 4.4% in ν max and 0.35, 0.61 and 1.3% in Δν, with the ν max uncertainties close to their nominal coverage. The fully automated pipeline was applied to 274,721 TESS red-giant candidates observed for at least ten sectors, the framework returning reliable ν max for 56,224 stars and reliable Δν for 39,939, taking 8 ms per star. The sample follows Δν= 0.278 ν max 0.757 with a scatter of 6.2%, and the red-clump phase stands out as a distinct overdensity, although neither module is provided an evolutionary label or asked to produce one.