Automatic alignment of multi-temporal images of planetary nebulae using local optimization

Automatic alignment of time-separated astronomical images have historically proven to be difficult. The main reason for this difficulty is the amount of sporadic and unpredictable noise associated with astronomical images. A few examples of these effects are: image distortion due to optics, cosmic ray hits, transient background sources (super novae) and various artifact sources associated with the CCD imager itself. In this paper a new automated image registration method is introduced for aligning two time-separated images while minimizing the inherent errors and unpredictabilities. Using local optimization, the two images are aligned when the root mean square of the difference between the two images is minimized. The dataset consists of images of galactic planetary nebulae acquired by the Hubble Space Telescope. The aligned centroids inferred by the suggested method agree with the results from previously aligned images by inspection with high confidence. It is also demonstrated that this method is robust, sufficient, does not require extensive user input and it is highly sensitive to minor adjustments.