The Pillars of Creation in the Eagle Nebula (M16) remain one of the iconic images of the Hubble Space Telescope. Three pillars rise from a molecular cloud into an enormous HII region, powered by the massive young cluster NGC 6611. Such pillars are common in regions of massive star formation, where they form as a result of ionization and stellar winds.
In a paper that will shortly be published in MNRAS, entitled “The Pillars of Creation revisited with MUSE: gas kinematics and high-mass stellar feedback traced by optical spectroscopy,” McLeod et al (2015) analyze of new data acquired with the Multi Unit Spectroscopy Explorer (MUSE) instrument on the VLT. They used Montage to create integrated line maps of the single pointings obtained at the telescope. The figure below shows an example of these maps:
The authors confirmed the pillar tips are being ionized and photo-evaporated by the massive members of NGC 6611. They found a new bipolar outflow at the tip of the middle pillar and proposed that an embedded protostar is driving it. With the physical parameters and ionic abundances derived from the spectroscopic study, they estimated a mass loss rate due to photo-evaporation of 70 M⊙/Myr, which implies that these structures can expect to have a lifetime of 3 Myr.
Posted in astronomy, astronomy images, Astronomy software, Image mosaic, Image processing, Images, Integral Field Spectrographs, software, star formation
Tagged astronomy, Image mosaic, Image processing, Integral-field spectrographs, M16, Pillars of Creation, software, star formation
t the AAS meeting in January, I gave a presentation at a Special Session on Software Licenses about how and why we relicensed Montage from a proprietary license to a more permissive BSD 3-clause. You can see the short presentation below, and read the full text of the session here.
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