Speaker
Description
Recent studies show that Gaia Data Release 3 (DR3) low-resolution XP spectra can reliably recover bulk stellar properties like global metallicity. However, their coarse resolution limits our ability to classify objects based on specific spectral features. Tasks such as separating DA white dwarfs from A-type stars without reliable parallaxes or distinguishing broad from narrow emission lines to classify active galaxies are still better suited to moderate-resolution spectra like those from the Sloan Digital Sky Survey (SDSS).
In this work, we explore spectral super-resolution to bridge this gap, evaluating machine learning models designed to reconstruct SDSS-equivalent spectra directly from Gaia XP inputs. Because generative networks risk extrapolating false spectral features that could cause misclassifications, we prioritize establishing a reliable measure of confidence for the generated output. We investigate deterministic neural network baselines and explore probabilistic approaches, such as Monte Carlo Dropout, to output uncertainty estimates alongside the reconstructed flux.
To test this approach, we present preliminary evaluations of the model on a representative sub-classification task. We explore whether the network correctly flags low confidence when it encounters confusing or rare spectra, outlining a framework where machine-generated high-resolution features are bounded by a confidence measure to support robust science in large-scale surveys.