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Activity Number: 220444 - Astrostatistics Interest Group: Student Paper Award
Type: Topic Contributed
Date/Time: Thursday, August 12, 2021 : 10:00 AM to 11:50 AM
Sponsor: Astrostatistics Special Interest Group
Abstract #317521
Title: Reducing Ground-Based Astrometric Errors with Gaia and Gaussian Processes
Author(s): Willow Fox Fortino*
Companies: University of Delaware
Keywords: astrometry; sky noise; astronomy data analysis; machine learning; gaussian process
Abstract:

Stochastic field distortions caused by atmospheric turbulence are a fundamental limitation to the astrometric accuracy of ground-based imaging. This distortion field is measurable at the locations of stars with accurate positions provided by the Gaia DR2 catalog; we develop the use of Gaussian process regression (GPR) to interpolate the distortion field to arbitrary locations in each exposure. We introduce an extension to standard GPR techniques that exploits the knowledge that the 2-dimensional distortion field is curl-free. Applied to several hundred 90-second exposures from the Dark Energy Survey as a testbed, we find that the GPR correction reduces the variance of the turbulent distortions ?12×, 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.


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