Removing Internal Reflections from Deep Imaging Datasets

TL;DR

This paper introduces a method to characterize and remove internal reflections in astronomical imaging, improving the accuracy of deep surface photometry by reducing systematic artifacts caused by scattered light.

Contribution

It presents an empirical approach using calibration images to model and subtract internal reflections and scattered light in deep astronomical datasets.

Findings

Effective removal of internal reflections demonstrated on Virgo cluster data

Improved accuracy in deep surface photometry achieved

Method outperforms some existing scattered light mitigation strategies

Abstract

We present a means of characterizing and removing internal reflections between the CCD and other optical surfaces in an astronomical camera. The stellar reflections appear as out-of-focus images and are not necessarily axisymmetric about the star. Using long exposures of very bright stars as calibration images we are able to measure the position, size, and intensity of reflections as a function of their position on the field. We also measure the extended stellar point-spread function out to one degree. Together this information can be used to create an empirical model of the excess light from bright stars and reduce systematic artifacts in deep surface photometry. We then reduce a set of deep observations of the Virgo cluster with our method to demonstrate its efficacy and to provide a comparison with other strategies for removing scattered light.