Parallelization and Algorithmic Enhancements of High Resolution IRAS Image Construction

Abstract

The Infrared Astronomical Satellite carried out a nearly complete survey of the infrared sky, and the survey data are important for the study of many astrophysical phenomena. However, many data sets at other wavelengths have higher resolutions than that of the coadded IRAS maps, and high-resolution IRAS images are strongly desired both for their own information content and their usefulness in correlation studies. The HIRES program was developed by the Infrared Processing and Analysis Center (IPAC) to produce high-resolution (~ 1') images from IRAS data using the maximum correlation method (MCM). We describe the port of HIRES to the Intel Paragon, a massively parallel supercomputer, other software developments for mass production of HIRES images, and the IRAS Galaxy Atlas, a project to map the Galactic plane at 60 and 100 µm. Images produced from the MCM algorithm sometimes suffer from visible striping and ringing artifacts. Correcting detector gain offsets and using a Burg entropy metric in the reconstruction scheme were found to be effective in suppressing these artifacts. A variation of the destriping algorithm was used to subtract zodiacal emission.