Montage As A Visualization Engine

At the AAS Jamuary meeting, I presented a poster on how the Montage Image Mosaic Toolkit ( is increasingly being used as a visualization engine. The poster was designed by Angela Lerias (U.California, Riverside), who is interning with the Montage project.It gives three examples of visualization:

  • Creating large multi-color images for visualization;
  • Sky-coverage maps of wide area time-series photometric surveys; and
  • Integration of  Montage into the JS9 web based image display tool (


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The Montage Mosaic Engine and Visualization

Astronomy Computing Today

Those of you who have used Montage probably know it as a toolkit for processing images to create mosaics. It is, however, finding applicability in the visualization of images, as described in “The Application of Montage to the Visualization of Astronomical Images,” by Bruce Berriman and John Good. It is an Open Access paper published in a PASP Special Focus Edition on Visualization, edited by Brian Kent.

You can watch a video abstract of the paper here:

There are several areas where Montage brings value to visualization work, primarily because its design as a toolkit enables integration into software environments:

  • Integration into visualization environments, usually asa reprojection engine to co-register and re-project multi-wavelength images to a common set of image parameters, and represent them in a common coordinate system. Examples are JS9 and APLPy. Luciani et al 2014  integrated Montage into a highly-scalable client-server architecture intended as a…

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Creating Sky Coverage Maps With Montage

Most of you are familiar with how to create image mosaics with Montage. You can also use the mViewer module to create sky coverage maps. The image below, from Rodriguez et al. (2017) shows an example of this, created to show  how the KELT-N and KELT-S fields overlap with the Kepler and K2 fields.


The graphic can be created with a single call to mViewer:

mViewer -ct 1 \
        -color   "#808080"  \
        -grid     equ j2000 \
        -colorcol color \
        -imginfo  KELT_fields.tbl \
        -symbol   none \
        -labelcol label \
        -catalog  KELT_fields.tbl \
        -color    red \
        -imginfo  k2_footprints.tbl \
        -color    red \
        -imginfo  kepler_footprint.tbl \
        -gray     equatorial.fits -0.5s max gaussian-log \
        -out      KELT.png

mViewer itself  is a command-line tool (though it can be used in interactive visualization scenarios through Python and web GUIs). It can easily be embedded in pipelines and in conjunction with other Montage modules can provide adaptive support for building visualizations for presentation and reporting. mViewer overlay “commands” are actually a set of (repeatable) command-line directives, as will be illustrated here. The complete set of directives can be found in the mViewer documentation.

You can learn more about creating sky coverage graphics in the on-line tutorial at

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Expanding The Breadth of Use of the Montage Image Mosaic Engine


This is the title of a poster presented at the 2017 NSF Software Infrastructure for Sustained Innovation (SI^2) Principal Investigator (PI) workshop in February 2017 (an earlier version was presented at the ADASS conference in Trieste, Italy in October 2016). The poster emphasizes the benefits of the simple, flexible and sustainable design, and describes four new uses of Montage:

  • Supporting enhanced photometry with the Spitzer Extragalactic Representative Volume Survey (SERVS).
  • Creating mosaics of Gemini GEMS adaptive optics images: combining images of four chips into a single mosaic.
  • Sub-setting image cubes that are up to 1 TB each in the CSIRO ASKAP Science Data Archive (CASDA), which stores 5 petabytes/year.
  • Creating a full-resolution, five-color mosaic of the Herschel Hi-GAL Survey of the Galactic plane, for display on the Fiske Planetarium dome.

ADASS 2016-1_best_Sep28_editedMH copy


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TOASTing Your Images With The Montage Image Mosaic Engine

Our poster at the 229th AAS meeting, Grapevine TX, Jan 4-8 2017 shows how to prepare your images for consumption by the World Wide Telescope.


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Montage Helps Find Near-Earth Objects

One of the most interesting applications that I have found for Montage is by the Lincoln Near Earth Asteroid Research (LINEAR) program. A paper by Vighh et al. (2015) described how LINEAR has made significant contributions to the discovery of Near-Earth Objects (NEOs), thereby improving knowledge of the NEO size distribution and helping to characterize the threat to the Earth from NEOs.  AS well as contributing over 1.3 million new observations of known local objects, the project discovered 483 new NEOs, broken down as follows:


The team has integrated Montage into its processing pipeline. The observing cadence involves 15 min revisits to each observing field, during which time the field rotation caused by the telescopes Alt-Az mount is sufficiently large that the same CCD chip images from the five collected frames do not line up in image coordinates. Thus they use Montage to rotate and co-register all the images, so that are ready for source detection. Their Figure 6, shown below, shows a registered rectangular frameset after processing by Montage, and their Figure 7 shows an example detection of a moving object.




See “Initial asteroid detection results using the Space Surveillance Telescope,” by  Viggh, H.E.M, Ushomirsky, G. ; Stokes, G. ; Cornell, M. ; Ruprecht, J.D. ; Varey, J. ; Klein, A. ; Goldberg, M. 2015,

Posted in Asteroids, astronomy, astronomy images, Astronomy software, Image mosaic, Image processing, NEOs, Software engineering, Solar System | Tagged , , , , , , , | 1 Comment

Best Practices for Code Release: Experience With Montage

This is a presentation given at the  Special Session on Tools and Tips for Better Software, held at the 227th AAS Meeting, Kissimmee, FL, Jan 5, 2016. It was one of six presentations given at the session, whose purpose is described below.

“Research in astronomy is increasingly dependent on software methods and astronomers are increasingly called upon to write, collaborate on, release, and archive research quality software, but how can these be more easily accomplished? Building on comments and questions from previous AAS special sessions, this session, organized by the Astrophysics Source Code Library (ASCL) and the Moore-Sloan Data Science Environment at NYU, explores methods for improving software by using available tools and best practices to ease the burden and increase the reward of doing so. With version control software such as git and svn and companion online sites such as GitHub and Bitbucket, documentation generators such as Doxygen and Sphinx, and Travis CI, Intern, and Jenkins available to aid in testing software, it is now far easier to write, document and test code. Presentations cover best practices, tools, and tips for managing the life cycle of software, testing software and creating documentation, managing releases, and easing software production and sharing. After the presentations, the floor will be open for discussion and questions. “

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