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Willow Fortino, University of Delaware
Atmospheric turbulence is considered to be a fundamental limitation to ground-based astrometry. However, the Gaia (a space-based observatory) data release 2 (DR2) catalog presents an incredible opportunity to use machine learning techniques to improve astrometric measurements for stars that are heavily distorted due to atmospheric turbulence. We have developed the use of Gaussian process regression (GPR) to predict the atmospheric turbulence at any location of an image provided that there exists nearby stars in the Gaia DR2 catalog. Using ~300 90-second exposures from the Dark Energy Survey (DES), a six year survey of ~5000 deg^2 of the Southern sky, we find that the GPR correction reduces the variance of the turbulent distortions about 12 times, on average, with better performance in denser regions of the Gaia catalog. The RMS per-coordinate distortion in the riz bands is typically ~7 mas before any correction, and ~2 mas after application of the GPR model. The GPR astrometric corrections are validated by the observation that their use reduces, from 10 to 5 mas RMS, the residuals to an orbit fit to riz-band observations over 5 years of the r = 18.5 trans-Neptunian object Eris.