Performance analysis of the Least-Squares estimator in Astrometry

TL;DR

This paper analyzes the performance of the least-squares estimator in astrometry, comparing it with the Cramer-Rao bound, and finds it is near optimal in low signal-to-noise conditions but less efficient at high SNR, validated through simulations.

Contribution

It provides the first analytical bounds and approximations for the bias and mean-square-error of the least-squares estimator in astrometry, and characterizes its efficiency relative to the Cramer-Rao bound.

Findings

Least-squares estimator is near optimal in low SNR regimes.

Performance gap between least-squares and Cramer-Rao bound increases at high SNR.

Theoretical bounds are validated with simulated observations.

Abstract

We characterize the performance of the widely-used least-squares estimator in astrometry in terms of a comparison with the Cramer-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 is the least-squares estimator in comparison with the minimum variance Cramer-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 Cramer-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 Cramer-Rao bound. However, we also demonstrate that, in general, there is no unbiased estimator for the astrometric position that can precisely reach the Cramer-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 intra-pixel response changes are neglected, and where flat-fielding is achieved with very high accuracy.