The Application of the Montage Image Mosaic Engine To The Visualization Of Astronomical Images

May 2017 • 2017PASP..129e8006B

Authors • Berriman, G. Bruce • Good, J. C.

Abstract • The Montage Image Mosaic Engine was designed as a scalable toolkit, written in C for performance and portability across *nix platforms, that assembles FITS images into mosaics. This code is freely available and has been widely used in the astronomy and IT communities for research, product generation, and for developing next-generation cyber-infrastructure. Recently, it has begun finding applicability in the field of visualization. This development has come about because the toolkit design allows easy integration into scalable systems that process data for subsequent visualization in a browser or client. The toolkit it includes a visualization tool suitable for automation and for integration into Python: mViewer creates, with a single command, complex multi-color images overlaid with coordinate displays, labels, and observation footprints, and includes an adaptive image histogram equalization method that preserves the structure of a stretched image over its dynamic range. The Montage toolkit contains functionality originally developed to support the creation and management of mosaics, but which also offers value to visualization: a background rectification algorithm that reveals the faint structure in an image; and tools for creating cutout and downsampled versions of large images. Version 5 of Montage offers support for visualizing data written in HEALPix sky-tessellation scheme, and functionality for processing and organizing images to comply with the TOAST sky-tessellation scheme required for consumption by the World Wide Telescope (WWT). Four online tutorials allow readers to reproduce and extend all the visualizations presented in this paper.

Links

• SIMBAD http://simbad.u-strasbg.fr/simbo.pl?bibcode=2017PASP..129e8006B
• PDF https://iopscience.iop.org/article/10.1088/1538-3873/aa5456/pdf
• PDF https://iopscience.iop.org/article/http://iopscience.iop.org/article/10.1088/1538-3873/aa5456/pdf
• PREPRINT http://arxiv.org/abs/1702.02593
• DATA https://irsa.ipac.caltech.edu/bibdata/2017/B/2017PASP..129e8006B.html
• ELECTR http://iopscience.iop.org/article/10.1088/1538-3873/aa5456
• ELECTR https://iopscience.iop.org/article/10.1088/1538-3873/aa5456