The Next Generation of the Montage Image Mosaic Toolkit

• Main Authors: Berriman, G. Bruce, Good, J. C., Rusholme, B., Robitaille, T.
• Other Authors: Lorente, Nuria P. F., Shortridge, Keith, Wayth, Randall
• Format: Book Part
• Language: unknown
• Published: Astronomical Society of the Pacific 2016
• Subjects: Isi; sami
• Online Access: https://authors.library.caltech.edu/89605/; https://authors.library.caltech.edu/89605/2/1608.02649.pdf; https://resolver.caltech.edu/CaltechAUTHORS:20180913-082138550

The scientific computing landscape has evolved dramatically in the past few years, with new schemes for organizing and storing data that reflect the growth in size and complexity of astronomical data sets. In response to this changing landscape, we are, over the next two years, deploying the next generation of the Montage toolkit ([ascl:1010.036]). The first release (October 2015) supports multi-dimensional data sets ("data cubes"), and insertion of XMP/AVM tags that allows images to "drop-in" to the WWT. The same release offers a beta-version of web-based interactive visualization of images; this includes wrappers for visualization in Python. Subsequent releases will support HEALPix (now standard in cosmic background experiments); incorporation of Montage into package managers (which enable automated management of software builds), and support for a library that will enable Montage to be called directly from Python.This next generation toolkit will inherit the architectural benefits of the current engine - component based tools, ANSI-C portability across Unix platforms and scalability for distributed processing. With the expanded functionality under development, Montage can be viewed not simply as a mosaic engine, but as a scalable, portable toolkit for managing, organizing and processing images. The architectural benefits of Montage provide considerable flexibility to the end user, and we will describe how the community is taking advantage of it to integrate its components into pipelines and workflow environments. Examples include: underpinning a pipeline to create three color SDSS mosaics for galaxies in the RC3 catalogs; integration into the AAO/UKST SuperCOSMOS H-alpha Survey flux calibration pipeline (Frew et al. 2014); integration into the processing environment of the Sydney-AAO Multi-object Integral (SAMI) field spectrograph pilot survey; and integration into the processing environment for the Palomar Transient Factory. In addition, it is an exemplar tool for the development of cyberinfrastructure systems that will enable non-experts to run workflows at scale. One example is building AstroTaverna workflows with Virtual Observatory services. Another is the production, in collaboration with ISI/USC and Amazon Web Services, of a 16-wavelength Atlas of the Galactic Plane with Open Source tools such as the Pegasus Workflow management system which, when complete, is aimed at deploying a set of tools for scientists to process and manage data on distributed platforms.