I am delighted to say that we have received funding from the National Science Foundation (NSF) to deliver the next generation of the Montage Image Mosaic Engine. This new effort responds to the dramatic evolution in the computational landscape astronomy in the past few years. We will deliver, over the next two years:
- Support for data cubes.
- Support for two sky partitioning schemes, the Hierarchical Equal Area isoLatitude Pixelization (HEALPix), standard in cosmic background experiments; and the Tessellated Octahedral Adaptive Subdivision Transform (TOAST), used in immersive platforms such as the World Wide Telescope.
- A set of turnkey tools and associated tutorial that will enable astronomers who are not expert in distributed platforms and technologies to launch and manage processing at scale.
- A library that will allow Montage to be run directly from languages such as Python.
Montage has recently been relicensed, and is now available under a BSD 3-clause license. We will be making the code available on GitHub. We will also overhaul the web page and revive the Montage blog (here!).
The project staff are: Bruce Berriman (PI), John Good (Architect), Marcy Harbut (Documentation), Tom Robitaille and Ewa Deelman (collaborators). We are guided by a Users’ Panel consisting of Adam Ginsburg, August Muench and Suzanne Jacoby.
Just to whet your appetite, we show a short video that displays the structure of a molecular disk wind in HD 163296, measured by ALMA (PI: M. Rawlings). The video shows a re-projection by Montage of a data cube of the star that covers multiple velocities relative to the center of the CO J=3-2 line.
And here is a poster that describes some of the features we will be delivering, presented at the 2015 NSF SI2 PI Workshop, February 15 and 16 2015 in Arlington, VA.
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." 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.”
Posted in astronomy, astronomy images, Astronomy software, Image mosaic, Image processing, Images, Software engineering, star formation
Tagged astronomy, astronomy images, Chandra, Image mosaic, Image processing, Images, Spitzer, star formation
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.
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.
Posted in astronomy, astronomy images, Astronomy software, Chandra, software, X-ray astronomy
Tagged astronomy, astronomy images, Chandra, Image mosaic, Images, software, X-ray astronomy
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.
Posted in astronomy, astronomy images, Astronomy software, Image mosaic, Image processing, Images, Python programming, software, Software engineering
Tagged astronomy, astronomy images, Image mosaic, Image processing, Images, Python programming, software
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
Posted in astronomy, astronomy images, Astronomy software, Image mosaic, Image processing, Images, SMC, software
Tagged astronomy, astronomy images, Image mosaic, Image processing, Images, SMC, software
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.
Posted in astronomy, astronomy images, Astronomy software, Image mosaic, Image processing, software, Software engineering
Tagged astronomy, astronomy images, Image mosaic, Image processing, Images, software