2012 Argonne Conference


IMAGE PROCESSING FOR ASTRONOMICAL PHOTOGRAPHY

Elizabeth Butler1,  Arika Egan2,  Mark Jacobs*1

Northern Michigan University1, Physics, Marquette, MI 49855

Northern Michigan Unversity2, Physics, Marquette, MI 49855

mjacobs@nmu.edu

Abstract

Astronomers want images that accurately represent light levels in the night sky. CCD cameras can do this, but the images are affected by telescope optics and the camera’s electronics. Additionally, an image can be affected by tracking and atmospheric conditions. Optical vignetting, thermal noise in the camera, and digitization effects can be corrected with established techniques, using additional images of a uniform target (flat fielding), a no-light exposure (dark frame), and a no-time exposure (bias frame). If tracking and atmospherics are poor, then a process called track-and-stack can be used, where many short exposures are combined to create the effect of one long exposure. The resulting image can then be corrected using the above techniques. We discuss the application of these standard techniques to sample images from a laboratory and images taken of the night sky.

 


CREATING AN INFRASTRUCTURED FOR LSST ALL-SKY
CAMERA SITE DATA

Amelia E. Shirtz1,  Chuck Claver2,  Tim Axelrod3

Northern Michigan University1, Marquette, MI 49855

NOAO2, Tucson, AZ 85719

University of Arizona3, Tucson, AZ 85721

ddonovan@nmu.edu

Abstract

The science drivers behind the LSST project require that the LSST be able to observe in less than ideal conditions. Better knowledge of the structure function of clouds over the LSST site will assist with photometric calibration of LSST data. A program was created that identifies reference stars in an image from an all-sky camera located in Chile. The program then calculates an apparent magnitude and position data for the stars in the image and then these values are recorded. A pipeline was made to run an automated version of the program and to store the results in a database. Early light curves show that air mass extinction values can be extracted and cloud structures can be seen with the photometry data. The LSST project will be able to take the pipeline and use the output to model variations in clouds and air masses. Shirtz was supported by the NOAO/KPNO Research Experiences for Undergraduates (REU) Program which is funded by the National Science Foundation Research Experiences for Undergraduates Program and the Department of Defense ASSURE program through Scientific Program Order No. 13 (AST-0754223) of the Cooperative Agreement No. AST-0132798 between the Association of Universities for Research in Astronomy (AURA) and the NSF.