The MADGICS 8621{AA} DIB cat. from GaiaDR3 RVS

Conventional measurements of DIBs in stellar spectra are complicated by residuals between observations and best-fit stellar models. To overcome this, we simultaneously model the spectrum as a combination of stellar, dust, and residual components, with full posteriors on the joint distribution of the components.

This decomposition is obtained by modeling each component as a draw from a high-dimensional Gaussian distribution in the data space (the observed spectrum)-a method we call "Marginalized Analytic Data-space Gaussian Inference for Component Separation" (MADGICS). We use a data-driven prior for the stellar component, which avoids missing stellar features not well modeled by synthetic spectra. This technique provides statistically rigorous uncertainties and detection thresholds, which are required to work in the low signal-to-noise regime that is commonplace for dusty lines of sight.