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Spectro-Photometry and Data-Driven Abundance Calibration

Presentation #220.02 in the session “Machine Learning in Astronomy: Measuring the Properties of Stars with Machine Learning (Meeting-in-a-Meeting)”.

Published onJun 18, 2021
Spectro-Photometry and Data-Driven Abundance Calibration

The Gaia space mission has provided us with high-precision measurements of positions, velocities, and stellar parameters for millions of stars in the Milky Way, containing a wealth of information about our home galaxy and ushering in a new era in Galactic astronomy. However, beyond a few kpc distance from the Sun, uncertainties in the parallaxes of the observed stars dominate over all other measurement errors and fundamentally limit the precision of all studies of the Milky Way spanning large Galactocentric distances. To overcome these limitations we developed a data-driven model using machine-learning techniques to determine precise parallaxes by combining multi-band photometry and spectroscopy to estimate precise parallaxes for luminous red giant stars, which enable us make large kinematic maps of our Galaxy out to Galactocentric distances of ~25 kpc, well beyond the reach of Gaia parallaxes. Making use of this new data set we determine the circular velocity curve of the Milky Way, we uncover a spiral pattern in the radial velocity of stars possibly tracing the spiral structure of our Galaxy, and we measure the chemical composition of stars at their birth across the Milky Way disk.


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