Measuring the 8621 \r{A} Diffuse Interstellar Band in Gaia DR3 RVS Spectra: Obtaining a Clean Catalog by Marginalizing over Stellar Types
Andrew K. Saydjari, Ana Sof\'ia M. Uzsoy, Catherine Zucker, J. E. G., Peek, Douglas P. Finkbeiner

TL;DR
This paper introduces MADGICS, a novel Bayesian spectral decomposition method, to accurately measure the 8621 Å diffuse interstellar band in Gaia DR3 RVS spectra, overcoming stellar contamination and noise issues.
Contribution
The paper presents MADGICS, a new Gaussian inference technique for spectral component separation, enabling robust DIB measurements in low signal-to-noise stellar spectra.
Findings
Produced an improved, contamination-free 8621 Å DIB catalog from Gaia DR3 data.
Constrained the DIB's rest wavelength with high precision.
Found no significant DIB detection in the Local Bubble region.
Abstract
Diffuse interstellar bands (DIBs) are broad absorption features associated with interstellar dust and can serve as chemical and kinematic tracers. 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…
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Taxonomy
TopicsAtmospheric Ozone and Climate · Atmospheric and Environmental Gas Dynamics · Spectroscopy and Laser Applications
