Montage Used To Study The Structure of the Star-forming Cluster RCW 38

E. Winston et al. (2011) report that they used  Montage in their recent paper “The Structure of the Star-forming Cluster RCW 38.” This was a multiwavelength investigation that used Spitzer, Chandra and 2MASS data that probed the spatial distribution of the young stellar population in this high mass star-formation region.

The RCW 38 region observed with IRAC on Spitzer. The plot shows a three-band false color image of the cluster, where the mosaic at each wavelength was created from the four epochs of data combined using the Montage mosaicing software. The field shows the overlap region of the four IRAC bands. Blue is 3.6μm, green is 4.5μm, and red is 8.0μm. The reddish hue at 8.0μm is due mainly to diffuse PAH emission. Emission from shocked hydrogen is visible in green. The outline of the Chandra ACIS-I field of view is overlaid as a white square.

"The RCW 38 region observed with IRAC on Spitzer. The plot shows a three-band false color image of the cluster, where the mosaic at each wavelength was created from the four epochs of data combined using the Montage mosaicing software. The field shows the overlap region of the four IRAC bands. Blue is 3.6μm, green is 4.5μm, and red is 8.0μm. The reddish hue at 8.0μm is due mainly to diffuse PAH emission. Emission from shocked hydrogen is visible in green. The outline of the Chandra ACIS-I field of view is overlaid as a white square." From Winston et al (2011)

They found: “..624 YSOs: 23 class 0/I and 90 flat spectrum protostars, 437 Class II stars, and 74 Class III stars. We also identify 29 (27 new) O star candidates over the IRAC field. Seventy-two stars exhibit IR-variability, including seven class 0/I and 12 flat spectrum YSOs. A further 177 tentative candidates are identified by their location in the IRAC [3.6] vs. [3.6]-[5.8] cmd. We find strong evidence of subclustering in the region. Three subclusters were identified surrounding the central cluster, with massive and variable stars in each subcluster. The central region shows evidence of distinct spatial distributions of the protostars and pre-main sequence stars. A previously detected IR cluster, DB2001 Obj36, has been established as a subcluster of RCW 38. This suggests that star formation in RCW 38 occurs over a more extended area than previously thought. The gas to dust ratio is examined using the X-ray derived hydrogen column density, NH and the K-band extinction, and found to be consistent with the diffuse ISM, in contrast with Serpens & NGC1333. We posit that the high photoionising flux of massive stars in RCW 38 affects the agglomeration of the dust grains.”

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Montage and the VAO E/PO Plan

Montage is one of the tools that the U.S. Virtual Astronomical Observatory project expects to use in bringing the Virtual Observatory into the classroom. The Virtual Observatory (VO) is an international effort to bring a large-scale electronic integration of astronomy data, tools, and services to the global community. See the graphic below, a poster on the subject by Brandon Lawton, Bonnie Eisenhamer, Barbara Matson and Jordan Raddick.

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X-ray Observations of Radio Transients without Optical Hosts

Montage was recently used by Croft, Tomsick and Bower in their study of a VLA archival calibration field. They used Chandra observations to attempt to identify X-ray coun- terparts to the eight transient sources without optical counterparts, and two transient sources known to have optical counterparts. They were able to identify a marginal X-ray detection of one source. They concluded that the data are consistent with the view that the optically-undetected radio transients are flares from isolated old Galactic neutron stars.

Postage stamp images of sources in this study. From top left to bottom right, in order of increasing wavelength: Chandra; GALEX far-UV and near-UV; POSS-II Bj, Rc, and Ic; J, H, and Ks from B07; WISE channels 1 – 4. Overlaid on each image are radio contours are from 150 – 250 μJy beam−1 in steps of 50 μJy beam−1 for the single-epoch VLA D-array data for 5T7.

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A Python Wrapper for the Montage Mosaic Engine

Montage is written in C for performance, but there are many Python programmers in astronomy who have asked if they can use Montage with Python. Yes, it turns out they can, through the good offices of Tom Robitaille at the Center for Astrophysics. He has written Python-montage, a python module that “provides a Python API to the Montage Astronomical Image Mosaic Engine, including both functions to access individual Montage commands, and high-level functions to facilitate mosaicking and reprojecting.” Tom’s release page, which includes a gzipped tar file for download, describes how to install the module and provides an example of how to use it.

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Montage Used in “Surveying the Agents of Galaxy Evolution”

Surveying the Agents of Galaxy Evolution (SAGE) is a project led by Margaret Meixner of STScI, and it is one of the Spitzer Space Telescope Legacy projects, considered of unusually high science value. The project uses the Small Magellanic Cloud (SMC) as a laboratory for investigating the evolution of the interstellar medium, as it is replenished by dying stars and then recycled in new generations of stars. SAGE has made wide use of Montage, and has used it in their latest paper “Surveying the Agents of Galaxy Evolution in the Tidally-Stripped, Low Metallicity Small Magellanic Cloud (SAGE-SMC). I. Overview.”

They used Montage to make mosaics of data at different wavelengths with the Spitzer MIPS and IRAC instruments and place them on a common pixel sampling, projection and coordinate system

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A Montage Word Cloud

I thought I would have a change of scenery this week and have a little fun. The other week, I posted a word cloud for my other blog, Astronomy Computing Today, so I went to the wordle web site and created a word cloud for the Montage blog. A word cloud takes the words in the blog posts since I started the blog in May 2010, and creates a graphic that gives greater prominence to words that appear more frequently in the source text.

Montage is prominent, but so are pixel and flux, rather than software engineering and computing terms. This likely reflects that the posts are more about astronomical applications of Montage and how astronomers use it, rather than the architecture of the application.

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The Difference Between The Mosaicking Features of Skyview and Montage

One of the questions we are often asked is what is the difference between Montage and the mosaic capabilities offered by the  Skyview Virtual Telescope at NASA’s High Energy Astrophysics Archive Research Center.

My colleague John Good, the Montage architect, describes the difference as follows:

“Montage is much more slavishly flux conserving than Skyview. By default Skyview uses an interpolation between neighbor input pixels to determine the output value of a pixel.  It only has one mode that is close to flux conserving and this only works exactly for certain projections where pixel edges can be treated as straight lines even after reprojection (though not a bad approximation most of the time).

Montage has been extensively tested and shown to conserve flux (to the floating-point round-off limit) for all projections.  Pixel edges are treated as spherical coordinate curves.  Also, reprojected areas for fractional pixels are preserved exactly so there are no edge effects at all when mosaicking.

Montage can be used with all standard projections in the WCS libraries. Going by the Skyview documentation, it only supports seven.  Similarly, Skyview only supports a few specific coordinate systems while Montage allows full precession (e.g. Ecliptic B1983.5 -> Equatorial J2011.0).

Montage includes an extensive set of tools for background matching over the complete set of overlapping input images based on an iterative relaxation technique

Montage is written in C for speed. The newer version of Skyview is written in Java.  When dealing with complex spherical trigonometry, C is much faster.  Skyview often looks fast, but this is mostly due to the default resampling and background handling.

Finally, Montage is broken down into a set of modules that can be
run intependantly and processing can therefore be heavily parallelized with very little effort.  In fact, Montage has been used extensively by the IT community to test large-scale parallelization and workflow environments.”

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