A Comparison Between Lucky Imaging and Speckle Stabilization for Astronomical Imaging

TL;DR

This paper compares Speckle Stabilization and Lucky Imaging for ground-based astronomical imaging, evaluating their efficiencies and sensitivities through modeling, and finds their relative advantages depend on system configurations and target brightness.

Contribution

It provides a detailed comparison of Speckle Stabilization and Lucky Imaging, including modeling results that quantify their relative efficiencies and sensitivities under various conditions.

Findings

Speckle Stabilization is 3.35 times more efficient than shift-and-add.

Speckle Stabilization detects targets 1.42 magnitudes fainter.

Optimal Lucky Imaging can outperform Speckle Stabilization in efficiency and sensitivity.

Abstract

The new technique of Speckle Stabilization has great potential to provide optical imaging data at the highest angular resolutions from the ground. While Speckle Stabilization was initially conceived for integral field spectroscopic analyses, the technique shares many similarities with speckle imaging (specifically shift-and-add and Lucky Imaging). Therefore, it is worth comparing the two for imaging applications. We have modeled observations on a 2.5-meter class telescope to assess the strengths and weaknesses of the two techniques. While the differences are relatively minor, we find that Speckle Stabilization is a viable competitor to current Lucky Imaging systems. Specifically, we find that Speckle Stabilization is 3.35 times more efficient (where efficiency is defined as signal-to-noise per observing interval) than shift-and-add and able to detect targets 1.42 magnitudes fainter when using a standard system. If we employ a high-speed shutter to compare to Lucky Imaging at 1% image selection, Speckle Stabilization is 1.28 times more efficient and 0.31 magnitudes more sensitive. However, when we incorporate potential modifications to Lucky Imaging systems we find the advantages are significantly mitigated and even reversed in the 1% frame selection cases. In particular, we find that in the limiting case of Optimal Lucky Imaging, that is zero read noise {\it and} photon counting, we find Lucky Imaging is 1.80 times more efficient and 0.96 magnitudes more sensitive than Speckle Stabilization. For the cases in between, we find there is a gradation in advantages to the different techniques depending on target magnitude, fraction of frames used and system modifications.