Robust Registration of Astronomy Catalogs with Applications to the Hubble Space Telescope

Catalog cross-registration;Astrometry;Robust statistics;Algorithm;Applied Mathematics & Statistics

Tian, FanBudavari, Tamas ;

Johns Hopkins University

关键词: Catalog cross-registration;    Astrometry;    Robust statistics;    Algorithm;    Applied Mathematics & Statistics;   

Others  :  https://jscholarship.library.jhu.edu/bitstream/handle/1774.2/60172/TIAN-THESIS-2018.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: JOHNS HOPKINS DSpace Repository

【 摘 要 】

Astrometric calibration of images with a small field of view is often inferior to the internal accuracy of the source detections due to the small number of guide stars in the images. One important experiment with such challenges is the Hubble Space Telescope (HST). A possible solution is to cross-calibrate overlapping fields instead of just relying on standard stars. Following the study in Budavári and Lubow (2012), we use infinitesimal 3D rotations for fine-tuning the calibration but re-formalize the objective to be robust to a large number of false candidates in the initial set of associations. Using Bayesian statistics, we accommodate bad data by explicitly modeling the quality which yields a formalism essentially identical to M-estimation in robust statistics. Our preliminary results on simulated catalogs show great potentials for improving the HST calibration.