Deep Co-Added Sky from Catalina Sky Survey Images
Akshat Singhal, Varun Bhalerao, Ashish A. Mahabal, Kaustubh Vaghmare,, Santosh Jagade, Sumeet Kulkarni, Ajay Vibhute, Ajit K. Kembhavi, Andrew J., Drake, S George Djorgovski, Matthew J. Graham, Ciro Donalek, Eric, Christensen, Stephen Larson, Edward C. Beshore

TL;DR
This paper presents the creation of deep, co-added sky images from Catalina Sky Survey data, significantly enhancing the depth and quality of baseline images for transient detection in a large sky area.
Contribution
It introduces a methodology for stacking nearly 0.8 million images over ten years, achieving deeper sky images without standard filters, and compares software options for optimal co-adding.
Findings
Deep images reach magnitude 22.0–24.2 across the sky.
Stacking 200 images yields an AB magnitude sensitivity of 22.8.
The methodology is applicable to other panoramic surveys.
Abstract
A number of synoptic sky surveys are underway or being planned. Typically they are done with small telescopes and relatively short exposure times. A search for transient or variable sources involves comparison with deeper baseline images, ideally obtained through the same telescope and camera. With that in mind we have stacked images from the 0.68~m Schmidt telescope on Mt. Bigelow taken over ten years as part of the Catalina Sky Survey. In order to generate deep reference images for the Catalina Real-time Transient Survey, close to 0.8 million images over 8000 fields and covering over 27000~sq.~deg. have gone into the deep stack that goes up to 3 magnitudes deeper than individual images. CRTS system does not use a filter in imaging, hence there is no standard passband in which the optical magnitude is measured. We estimate depth by comparing these wide-band unfiltered co-added images…
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