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:

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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.

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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,  http://ieeexplore.ieee.org/xpl/abstractAuthors.jsp?arnumber=7118951

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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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Montage Poster at 227th AAS Meeting, Kissimmee, FL. Jan 4-8, 2016.

2016W_AAS_Poster copy

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Montage YouTube Channel Rolled Out!

We have released the Montage youTube channel at https://www.youtube.com/channel/UCFjmHCDrq4YIUly1r082TjA  . We have posted four videos of image cubes created with Montage, and will be posting more in the coming months.  Here is one example:

This is a full-resolution mosaic of the central 256 frequency planes of 30 GALFA-HI images, centered on 0h Right Ascension. The RGB color is derived by combining 3 adjacent frequency planes. All gaps, such as that around 20 degrees declination, are due to incomplete coverage in the input images.

http://montage.ipac.caltech.edu

Video created by Dr. B Rusholme. Mosaic derived on the Amazon EC2 cloud of Amazon Web Services, with an Education Credits Grant awarded through the “AWS SKA AstroCompute Program.”

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Version 4.0 of the Montage Image Mosaic Engine Released: Data Cubes and more.

Version 4.0 is a major upgrade of Montage, released with a BSD 3-clause license. The distribution is available from Git Hub at https://github.com/Caltech-IPAC/Montage and from the Montage web page at http://montage.ipac.caltech.edu/docs/download.html.

The release supports aggregation of of data stored as data cubes (actually, multidimensional data sets with four dimensions) into mosaics, and a new command-line visualization tool. Details are a follows:

    • Five new modules dedicated to aggregating multidimensional input images in FITS format into mosaics of data cubes, and to supporting management and analysis of these cubes and their associated metadata. These modules are:
      1. mTranspose: Re-orders axes of multi-dimensional data sets.
      2. mProjectCube: Reprojects a single cube to the scale and coordinate system specified by the user; it supports all projections in the World Coordinate System (WCS) library; and it supports the “Drizzle” algorithm.
      3. mSubCube: Creates a subimage (“cutout”) of a cube.
      4. mShrinkCube: Reduces the size of a FITS cube according an input scaling factor.
      5. mAddCube: Co-adds the reprojected cubes to form the output mosaic.

All but mTranspose are analogs of modules for creating two dimensional mosaics.

    • Backwards-compatible updates to existing modules to support processing of data cubes.
    • A new module, mViewer, supports rendering from the command line of multi-dimensional images as well as large-scale images. It creates JPEG and PNG output files. The JPEG files contain AVM tags, which support incorporation of the images into the WorldWide Telescope (WWT) and other E/PO tools.
    • A beta version of a Python wrapper around mViewer to support incorporation into Python processing environments.
    • A tutorial on transposing the axes of data cubes using mTranspose
    • A tutorial on creating a mosaic from data cubes.

As with earlier releases, the new release is written in ANSI-compliant C and intended for use on all common Unix-based platforms. It was tested formally on RedHat Enterprise Linux Server 5.9 and on Mac OS X 10.9.x, with the gnu cc complier version 4.1, and the primary test data sets were public data cubes measured with the OSIRIS integral field spectrograph at the Keck Observatory and cubes released by the Galactic Arecibo L-band Feed Array HI (GALFA-HI) Survey.

Sample image

This image represents an average of the central 10 velocity planes of a mosaic of five data cubes released as part of the Galactic Arecibo L-band Feed Array HI (GALFA-HI) survey (Peek et al., 2011, Ap J Suppl, 194, 20; DOI 10.1088/0067-0049/194/2/20; ADS Bibcode 2011ApJS..194…20P). GALFA is a high-resolution (~4′), large-area (13,000 deg2), high spectral resolution (0.18 km s-1), and wide band (-700 km s -1 < v LSR < +700 km s-1) survey of the Galactic interstellar medium in the 21 cm line hyperfine transition of neutral hydrogen conducted at Arecibo Observatory. See the Data Cube Mosaics tutorial on how to compute a data cube mosaic such as this.

GALFAcenter

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Montage used in the generation of the STARBIRDS Archive

McQuinn, Mitchell and Skillman (2015) recently published “The Panchromatic STARBurst IRregular Dwarf Survey (STARBIRDS): Observations and Data Archive.” They combined new and archival multi-wavelength observations for 20 nearby starburst and post-starburst dwarf galaxies to create a new archive of calibrated, homogeneously reduced images: the  “STARBurst IRregular Dwarf Survey” archive. They aggregated   images from the Galaxy Evolution Explorer Telescope (GALEX), the Hubble Space Telescope (HST), and the Spitzer Space Telescope (Spitzer) Multiband Imaging Photometer instrument. The data sets include flux calibrated, background subtracted images, al co-registered to the same world coordinate system.

The team used the Montage module mJPEG to create grayscale preview JPEG files of all the FITS files – you can see some examples below.  The JPEGs were made using a Gaussian stretch of the full range of the original image up to a maximum flux level of 99.999% of all pixel values. One of the benefits of the Montage toolkit design is that components such as mJPEG can be included in a script or program for bulk creation of images.

Visit the archive at http://groups.physics.umn.edu/starburst-dwarf/_galaxy_sample.php.

Here are some sample images:

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Montage Supports the Kepler Follow-Up Observation Program.

The Kepler Community Follow-up Program, known also as the Kepler Follow-up Observation Program and KFOP, is a program instituted to conduct follow-up observations on Kepler Objects of Interest (KOI), or signals observed by the Kepler spacecraft that may indicate the presence of a planet transiting its host star. Because  the transit method of finding planets tends to produce a large number of false positives, KFOP is intended to rule out false positives amongst the KOIs and confirm more discoveries of exoplanets.

The NASA Exoplanet Archive, in support of the KFOP, has utilized Montage to generate a finding chart and nearby source catalog for all of the Kepler planetary candidate systems.  The UKIRT Observatory had performed a J-band survey of the Kepler Field and generated a source detection catalog; there are 1100+ individual images with 16 million sources in the survey.  Montage was used to index the images and the catalog (using an R-tree indexing scheme) to enable rapid determination of the best image for each Kepler target and to create a list of sources within 30″ of the Kepler targets.  Montage was then used to orient all of the best images and generate 1 arcmin cutout images with jpeg previews, overlayed with an equatorial coordinate grid: an example is shown below.

The modular design of Montage enabled the processing to be run within IDL and further analyzed, all within a scripted environment.  The usefulness of the UKIRT to the Kepler Project and the Follow-Up program was greatly enhanced by the UKIRT products, and those products were easily generated and made available to the team through Montage.

 

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This blogpost was based on material provided to me by Dr. David Ciardi.

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