Diffuse Interstellar Bands in Gaia DR3 RVS spectra Machine-learning based new measurements

• Published in: arXiv.org
• Main Authors: Zhao, He, Schultheis, Mathias, Qu, Caixia, Zwitter, Tomaz
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 18.12.2023
• Subjects: Celestial bodies; Diffuse interstellar bands; Hydrogen; Interstellar chemistry; Machine learning; Quality control; Radial velocity; Spectra
• ISSN: 2331-8422

Diffuse interstellar bands (DIBs) are weak and broad interstellar absorption features in astronomical spectra originating from unknown molecules. To measure DIBs in spectra of late-type stars more accurately and more efficiently, we developed a Random Forest model to isolate the DIB features from the stellar components and applied this method to 780 thousand spectra collected by the Gaia Radial Velocity Spectrometer (RVS) that were published in the third data release (DR3). After subtracting the stellar components, we modeled the DIBs \(\lambda\)8621 and \(\lambda\)8648. After quality control, we selected 7619 reliable measurements. The rest-frame wavelength of DIB \(\lambda\)8621 was updated as \(\lambda_0\,{=}\,8623.141\,{\pm}\,0.030\) AA in vacuum, corresponding to 8620.766 AA in air, which was determined by 77 DIB measurements toward the Galactic anti-center. With the peak finding method and a coarse analysis, DIB \(\lambda\)8621 was found to correlate better with the neutral hydrogen than the molecular hydrogen (represented by \(^{12}\)CO \(J\,{=}\,(1{-}0)\) emission). We also obtained 179 reliable measurements of DIB \(\lambda\)8648 in the RVS spectra of individual stars for the first time, further confirming this very broad DIB feature. A rough estimation of \(\lambda_0\) for DIB \(\lambda\)8648 was 8646.31 AA in vacuum, corresponding to 8643.93 AA in air, assuming that the carriers of \(\lambda\)8621 and \(\lambda\)8648 are co-moving. We confirmed the impact of stellar residuals on the DIB measurements in Gaia DR3, which led to a distortion of the DIB profile and a shift of the center (\(\lesssim0.5\) AA), but the EW was consistent with our new measurements. With our measurements and analyses, we propose that the machine-learning-based approach can be widely applied to measure DIBs in numerous spectra from spectroscopic surveys.