Performance Analysis of the Least-Squares Estimator in Astrometry. (4th January 2016)

• Record Type: Journal Article
• Authors: Lobos, Rodrigo A.; Silva, Jorge F.; Mendez, Rene A.; Orchard, Marcos
• Is Part Of: Publications of the Astronomical Society of the Pacific. Volume 127:Number 957(2015)
• Page Start: 1166
• Page End: 1182
• Publication Date: 2016-01-04
• Subjects: Astronomy -- Periodicals; Astronomy; Periodicals; Periodicals; 520.5
• DOI: 10.1086/683841
• Languages: English
• ISSNs: 0004-6280

Abstract

Abstract: We characterize the performance of the widely used least-squares estimator in astrometry in terms of a comparison with the Cramér-Rao lower variance bound. In this inference context the performance of the least-squares estimator does not offer a closed-form expression, but a new result is presented (Theorem 1) where both the bias and the mean-square-error of the least-squares estimator are bounded and approximated analytically, in the latter case in terms of a nominal value and an interval around it. From the predicted nominal value, we analyze how efficient the least-squares estimator is in comparison with the minimum variance Cramér-Rao bound. Based on our results, we show that, for the high signal-to-noise ratio regime, the performance of the least-squares estimator is significantly poorer than the Cramér-Rao bound, and we characterize this gap analytically. On the positive side, we show that for the challenging low signal-to-noise regime (attributed to either a weak astronomical signal or a noise-dominated condition) the least-squares estimator is near optimal, as its performance asymptotically approaches the Cramér-Rao bound. However, we also demonstrate that, in general, there is no unbiased estimator for the astrometric position that can precisely reach the Cramér-Rao bound. We validate our theoretical analysis through simulated digital-detector observations under typical observing conditions. We show that the nominal value for the mean-square-error of the least-squares estimator (obtained from our theorem) can be used as a benchmark indicator of the expected statistical performance of the least-squares method under a wide range of conditions. Our results are valid for an idealized linear (one-dimensional) array detector where intrapixel response changes are neglected, and where flat-fielding is achieved with very high accuracy.