SkyView V3.1.4: GALEX update, Memory fix

A user (thanks Steve U!) brought to our attention a problem with the GALEX survey in SkyView. SkyView uses the GALEX archive at MAST and only retrieves files when a user first requests data in a region. We keep files in a cache, so we never request the same file twice. Only about 1/2 of the GALEX GR6 data are cached, so user requests often require us to retrieve data from MAST. It turns out that MAST reprocessed a bit under 1% of the GR6 data after we got the URL locations, which meant that the URLs that we were using to point to the data were out-of-date. If we already had the image in our cache all was copacetic, but an attempt to download an updated file caused a complete failure for that survey.

Version 3.1.4 of SkyView has been released with updated survey description files for the two GALEX surveys and should address this problem. Users of the SkyView-in-a-Jar should download the latest jar if they want to retrieve GALEX data.

In testing this out, I noticed that SkyView was failing due to memory problems when trying to tile together a large number of images. This was due to a failure to deallocate memory when we were using 4-byte reals in the input images. [That was put in recently to allow us to handle larger input images.] That’s fixed too though we haven’t heard of anyone having problems with it.

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Release 3.1.3: SkyView, DS9, Aladin and SAMP

For some while we’ve supported the Virtual Observatory Simple Application Messaging Protocol (SAMP) for SkyView when you are using the jar file. The idea is that you can run a command like:

java -jar skyview.jar position=3c273 survey=dss samp=true

and any SAMP-aware application will be notified about the new image. Such tools include Aladin and the popular DS9 image viewer (and many others). Using SAMP you can pop an image directly into these interfaces. However we weren’t including the SAMP library so you needed to add this into the Java class path to get this to work.

Another thing you had to worry about is that SAMP needs a running SAMP hub. The way SAMP works is that all of the coordinated tasks talk to a single hub task which relays messages as appropriate. Some tools, e.g., Aladin, start up a hub for you so that you don’t need to worry about this, but others, including DS9 do not.

We’ve added the full JSAMP 1.2 JAR (developed by Mark Taylor at Bristol University) inside the SkyView jar to version 3.1.3 of SkyView. That’s pretty much the only change to this version. This not only makes it easier to use the capability — you don’t need to link an outside JAR — it also includes the code for a SAMP hub, so you can use the SkyView jar to start one up for you.

So to push images to Aladin — or any task that starts its own hub — just start Aladin (or other task) and then run SkyView requests.


java -jar Aladin.jar &

java -jar skyview.jar position=3c273 survey=dss
java -jar skvyiew.jar position=3c274 survey=dss

To send data to DS9, we add a command to start up a hub using the JSAMP code included in the SkyView jar:

java -cp skyview.jar org.astrogrid.samp.hub.Hub &
ds9 &

java -jar skyview.jar position=3c273 survey=dss
java -jar skvyiew.jar position=3c274 survey=dss

In both cases the generated images should magically appear in the viewing applications.

In our examples we’ve run the commands in sequence in the same shell, but you can use different windows if you prefer, but they must all be running on the same machine. You should start the Hub before you start DS9. It’s OK to use the second approach with tasks that run their own hub. Hubs’ initialization code checks to see if a hub is already running and they won’t start up if one is found.

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V3.1.2 released. Fixes to Mellinger survey

Version 3.1.2 of SkyView has been released.

Three bugs all related to the Mellinger optical surveys have been fixed in this latest release. All relate to use of the the Mellinger data. The errors were present only in images of less than 30 degrees and should not have affected all-sky images. Since release 3.0.0 the high resolution data was improperly scaled. Mellinger data is byte-scaled to values from 0-255, but values > 127 had 256 subtracted from them, so the high values were showing up as negative values. Since June 2012 the red and blue Mellinger high resolution data were transposed. Finally, due to a formatting error in the survey description file, users could not use the default values for the size (in degrees) of the field of view. The query failed without producing any image. Any queries which specified a field of larger than 30 degrees on a size should not have been affected by these problems.

We apologize for the errors which should have been caught by our regression tests — these have been updated. Many thanks for Ignacio Cisneros for graciously but persistently pointing out the problems.

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SkyView Surveys Summary

With the inclusion of multiple GOODS surveys we thought it might be useful to take a look at the overall properties of SkyView surveys. So here’s a summary of what we have in a few graphs.

With the addition of the HST ACS data we now have resolutions better than 0.1″ for some surveys.

The relatively miniscule coverage of the GOODS surveys is manifest in this plot.skycov

The sensitivity (here the product of the frequency and the limiting flux in Jy) is shown here

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SkyView Version 3.1.1: New GOODS Surveys

We have just released SkyView Version 3.1.1 which has a number of new surveys in the north and south GOODS regions.
With this release SkyView includes GOODS data from the following

  • Space Observatories:
    • Chandra ACIS
    • Hubble ACS
    • Hubble NICMOS
    • Spitzer MIPS
    • Spitzer IRAC
    • Herschel
  • Ground Observatories:
    • VLA
    • APEX LABDOCA sub-millimeter bolometer
    • NOAO Mayall Telescope
    • Subaru
    • Hawaii 2.2 m

A total of 33 distinct survey datasets of the GOODS regions including data in the radio, millimeter, infrared, optical, ultraviolet and X-ray regimes are available.

Please let us know of any other GOODS survey data the you would like to see incorporated into SkyView.

This release also includes a bug fix in the handling of the GLS projection. The GLS is simply a shifted version of the SFL projection, but the shift was not being properly accommodated in the projection. The APEX-LABDOCA survey uses this projection, but it had not been used before in any SkyView survey nor is it offered as an output choice. However users who downloaded the jar and tried to add local data in the GLS projection would have encountered the problem.

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We’ve just put out the preliminary version of SkyView V3.1.1 which has several more GOODS survey datasets:
8 new images of the north mostly from Gemini, three of south from the VLT ISAAC instrument, and five bands of Herschel data (though only 2 have data in the south). Once we’ve checked this out we’ll be formally releasing these surveys by making 3.1.1 the current version. But you can try them out right away by using specifying the version explicity — or just clicking on this link.

The Herschel data are relatively low resolution compared to most of the other GOODS surveys. The resolution decreases from about 6″ to 30″ as we go from 100 to 500 microns. Of course as with all the GOODS surveys only a tiny fraction of the sky is included. The resolution of the others is typically a bit better than 1″.

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SkyView V3.1.0 released

We’ve just released version 3.1.0 as the default version for SkyView. This includes 5 new GOODS surveys — a total of 13 new bands — and a little new infrastructure to support them as discussed in our previous articles. The new data include the highest resolution and deepest surveys in SkyView but cover only a very small fraction of the sly.

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SkyView GOODS preview

We are currently in the process of rolling out 5 new surveys including 13 separate bands to enable SkyView users to take advantage of some of the deepest surveys that have been made of the sky. This is the biggest single addition to SkyView’s surveys other than our initial setup twenty years ago. These are surveys made of the GOODS (Great Observatories Origins Deep Survey) regions centered on the Hubble and Chandra Deep Fields. Getting all of the metadata and regression tests set up is taking a while, but users can try out the surveys now at our Version 3.1.0 site. Soon this will be the default.

The surveys we are starting with include:

  • A VLA 1.4 GHz survey of the GOODS north field including data from all 4 VLA configurations.
    This data is combined to produce a single image of the field.
  • Spitzer MIPS 24 micron data of both the north and south
  • Four Spitzer IRAC surveys at 8.0, 5.8, 4.5, and 3.6 microns. Each of the north and south regions is split into two pieces for each wavelength. Only one image is available at 4.5 and 3.6 microns in the southern GOODS region, so that coverage is a little less in those bands.

    These data are provided in FITS files where only a fraction of the file is actually populated. We generated metadata that describes the rectangle inset in the FITS file where there is data. This rectangle is normally rotated with respect to the FITS image axes. We’ve added a new image finder that uses this metadata, skyview.process.imagefinder.RotatedRectangle

  • For the moment this means that the IRAC data cannot be accessed through SkyView-in-a-Jar but only at our Web site.

  • Four HST ACS observations in B, V, I and Z filters. These data are of extremely high resolution and are provided as a set of 18 ~0.25 GB image tiles.

    The full-resolution image tiles are quite large and it takes a fair while to process the full set for a given filter. To make it easy to intercompare this ACS data with lower resolution datasets, we developed SkyView‘s first scheme for hierarchical images where we combined pixels in the original data by factors of two to produce lower resolution data. When a user requests an ACS image, the lowest resolution image that has higher resolution than the user’s request is sampled. SkyView keeps the original data and pixel-combined data at factors of two up to 32. I.e., in our lowest resolution data each pixel is the average of a 32×32 pixel region in the original data — but that’s still better than 1″ per pixel. The standard XMLSurvey class was updated to support hierarchies generally, but so far this has only been used for the ACS.

    The same tiles are used for each resolution. A consequence is that images of large regions (i.e., 0.1 degrees or more) generally render faster since they are typically of lower resolution and can use the smaller files.

  • Three Chandra ACIS bands. This includes a soft 0.5-2 keV band, a hard 2-8 keV band and the full 0.5-8 keV band. This represents about 2 megaseconds exposure in the north and 4 megaseconds in the south.

These surveys include what is by far the highest resolution and deepest data in the far IR, optical and X-ray regimes available in SkyView. The resolution of VLA data is only modestly better than for the FIRST data, but the data is much deeper and better sampled. Of course nothing comes for free. The GOODS regions cover only about 0.01% of the sky.

We’ll be adding more GOODS surveys after this initial set — if you have a suggestion or preference please let us know. Currently HST NICMOS data and ground observations from Gemini and the VLT are in the queue.

To try out the new GOODS surveys just go to the V3.1.0 site. You’ll see all of the GOODS surveys listed at the end of the main survey list in their own box — we put them all together rather than breaking them out by regime. You can also use these surveys in the contours and they work very nicely in generating RGB images with each other. When we complete our metadata updates and checks on these data we’ll make 3.1.0 the current version.

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SkyView gets the GOODS

Next week we hope to begin releasing a whole new set of surveys in SkyView. These surveys are from the GOODS regions — the Great Observatories Origins Deep Survey. Two regions of the sky (northern and southern hemispheres), relatively devoid of stars, were selected for very deep observations by HST, Chandra, Spitzer and many other space and ground observatories. To make the surveys as deep as possible, only a tiny fraction of the sky (a few hundred arcminutes) are in the official GOODS regions, but some instruments have larger fields of view.

The GOODS data reflect a new approach for SkyView. We’ll be providing access to smaller regions and perhaps soon to non-survey observational data.

You can get a taste of the new data by looking our next version of SkyView. Look for the group of surveys labeled GOODS. Remember to enter coordinates in the GOODS regions. We’re in the process of testing right now. Here’s a color image (with something like real color) using Hubble ACS data of the Hubble deep field. Galaxies near and far predominate, with only a relative scattering of stars.

True-color image of Hubble Deep Field (HST ACS)

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Features in the Gallery: Who’s the culprit?

This image below (from 2014-01-19) isn’t a mystery. We’re seeing a bright star through some secondary optical path. The star is out of focus — as secondary images normally are — and we see shadows from the optical and support structures.

Recent Gallery Image

Can we tell what star it is? The nearest really bright star is Spica, which has coordinates (in decimal degrees) of about (RA,Dec)=(201.298,-11.161). This image is centered at (200.01,-9.356). That’s 2 degrees, a pretty long way away–maybe 4000 pixels in the image. But the DSS plates from which these data are taken are a full 6 degrees on a side, so Spica could be in the field of view.

In fact if we look at the plate that seems to enclose this location (labeled s719), it has a center at (200.658,-10.261). That’s just about the center of the two locations. It’s pretty common that there will be an artifact at a location like this with some kind of symmetry with the real location. So Spica it is. Sometimes the obvious answer is the right one.

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