Astronomical Image Processing: A Soft Computing Approach

Revision with unchanged content. In the past few years, pipelines providing astronomical data have been becoming increasingly important. The wide use of robotic telescopes has provided significant discoveries, and sky survey projects such as SDSS and the future LSST are now considered among the premier projects in the field astronomy. The huge amount of data produced by these pipelines raises the need for automatic processing. Astronomical pipelines introduce several well-defined problems, which challenge designers and operators of robotic telescopes and sky surveys. These include astronomical image compression, cosmic-ray hit rejection, transient detection, meteor triangulation, and association of point sources with their corresponding known stellar objects. This book describes applied soft computing algorithms that provide new or improved solutions to these growing problems in the field automated astronomical data processing. The algorithms discussed in this book enable automatic analysis of vast astronomical pipelines, and allow effective mining of the data for fundamental astronomical discoveries such as optical transients and stellar variability.

Dr. Lior Shamir: Completed his PhD in Computational Science at Michigan Technological University / Department of Physics. Research Fellow at the National Institute on Aging/National Institutes of Health, Baltimore, MD.