Speaker
Description
The low-resolution BP/RP (XP) spectra from Gaia Data Release 3 provide an unprecedented spectrophotometric dataset for hundreds of millions of sources, encoded as coefficients in a Hermite-function basis rather than as sampled fluxes. This representation poses both challenges and opportunities for machine learning (ML) applications. In this work, we investigate Gaia XP spectra as an object of study for supervised ML classification, with the goal of identifying representations that optimize performance, efficiency, and interpretability.
We begin by analyzing the standard Hermite-function basis used in Gaia DR3. We show that truncation of the coefficient space applied separately to the blue and red photometer components can improve classification performance across multiple metrics, indicating that a reduced representation mitigates noise and redundancy in the data. We then explore alternative orthogonal bases for representing XP spectra and demonstrate that these can yield improved dimensionality reduction while preserving relevant information for classification tasks.
Finally, we move beyond the traditional multivariate paradigm and investigate functional data analysis approaches, treating XP spectra as continuous functions. We find that such functional methods, though rarely applied in this context, provide substantial advantages: they enhance model interpretability and significantly reduce computational cost, while maintaining competitive predictive performance.