Tag Archive | Datasets

New Dataset from Galaxy Zoo!

We’ve posted a new data set here: http://data.galaxyzoo.org/#agn

This sample is presented in the Galaxy Zoo 1 paper on AGN host galaxies (Schawinski et al., 2010, ApJ, 711, 284). It is a volume-limited sample of galaxies (0.02 < z < 0.05, M_z < -19.5 AB) with emission line classifications, stellar masses, velocity dispersions and GZ1 morphological classifications. When using this sample, please cite Schawinski et al. 2010 and Lintott et al. 2008, 2011.

Download here: http://galaxy-zoo-1.s3.amazonaws.com/schawinski_GZ_2010_catalogue.fits.gz

Column definitions are as follows:

  • OBJID – SDSS object ID
  • RA, DEC – RA and Dec in J2000
  • REDSHIFT – SDSS spectroscopic redshift
  • GZ1_MORPHOLOGY – Galaxy Zoo 1 morphology according to the Land et al. (2008) “clean” criterion. GZ_morphology is an integer where 1-early type, 4-late type, 0-indeterminate, 3-merger
  • BPT_CLASS – 0-no emission lines, 1-SF, 2-Composite, 3-Seyfert and 4-LINER (see Schawinski et al. 2010 for details)
  • U,G,R,I,Z -SDSS modelMag extinction corrected but not k-corrected
  • SIGMA, SIGMA_ERR – Stellar velocity dispersion measured using GANDALF
  • LOG_MSTELLAR – log of stellar mass
  • L_O3 – Extinction-corrected [OIII] luminosity

Galaxy Zoo classifications in SDSS Database

The latest release of data from the Sloan Digital Sky Survey happened yesterday (SDSS3 blog article about the release). This has been widely talked about as providing the largest ever digital image of the sky, but one thing which might have passed your notice is that as part of this data release your Galaxy Zoo classifications (from the first phase of Galaxy Zoo) have been integrated into the SDSS public database (CasJobs). This will make GZ1 classifications all that more accessible for professional (and amateur?) astronomers to use in their research, and we hope to see some exciting and novel new uses coming out.

I’ll finish by including this visualization of the SDSS3 imaging data made by Mike Blanton and David Hogg (OK so I can’t work out how to embedd a YouTube video here, so here’s the link!).

SDSS3 Visualization

Preparing the pixels

At Zoo headquarters we like to be efficient. That means avoiding redoing work that has already been done by someone else. Particularly if those others have already spent a long time thinking how to do it best. Getting the images for the original Galaxy Zoo (way back in 2007!) was particularly easy. The fabulous Sloan Digital Sky Survey (SDSS) had already done all the work of taking the images, calibrating them, stitching them together, combining images at different wavelengths to make colour images, and optimising their appearance. All we had to do was ask their servers for an image, giving it the required location and size, and voilà, a image ready for adding straight into the Galaxy Zoo collection!

Life was rather more difficult when we added the special ‘Stripe 82′ images from the SDSS. For these, Galaxy Zoo team member Edd needed to do the stitching, combining, optimising, cutting-out and resizing. The details of how he did that are all here. We wanted to be able to compare the Stripe 82 images to the normal SDSS images, so we tried to keep things like the brightness scaling and appearance of colours as similar to the original as possible. Even so, it took us a couple of attempts to come up with a solution we were satisfied with.

With the Hubble data, as with Stripe 82, creating the images for the Zoo isn’t completely straightforward, but again most of the hard work had already been done for us. For the launch of Galaxy Zoo: Hubble, data was taken from several surveys:

We’ve also recently added in COSMOS: Cosmic Evolution Survey images – more about the nitty gritty details of those images in a future post.

The data calibration business was already taken care of by the science teams for each survey. The next steps, finding the galaxies, cutting out images at each available wavelength and combining them into colour images, was handled by Roger Griffith, who already had a system set up to do exactly that. Roger used a nifty piece of software called GALAPAGOS to manage the business of finding, cutting out and measuring the galaxies. The difference that Galaxy Zoo added to Roger’s system was that, like with Stripe 82, we wanted the properties of the colour images to match those from SDSS as closely as feasible, to enable us to compare the results from each of the Galaxy Zoo datasets as fairly as possible.

One particular issue with making colour HST images is that many surveys only produce data at two different wavelengths. Normally, colour images are made by choosing a different wavelength image for each of the three primary colours: red, green and blue. For the HST images we instead use one image for red, another for blue, and then just take the average of the two for green. The primary colours used in your computer display don’t usually match the colour filters that were used in the telescope at all, so the colours you see are only an indication of the true colour. Nevertheless the colours contain a lot of information: galaxies containing only old stars will look red, while those which are actively forming new stars will often be blue. Getting the images looking right, with fairly similar appearance to the SDSS images, required a cycle of testing and exchanging images back and forth, but we came to an agreement fairly quickly.

The HST images in Galaxy Zoo might not look as impressive as some of the press images you’ve seen from Hubble over the past twenty years. That’s because press images are usually picked specifically for their attractive appearance. The images chosen are often of nearby nebulae and galaxies for which HST allows us to see huge amounts of detail. The objects in Galaxy Zoo: Hubble are much more typical of the huge number of galaxies in HST surveys. Although HST can see much more detail than ground-based surveys, its mirror and field-of-view are smaller than most ground-based telescopes, so it can only cover a much smaller area of the sky in a reasonable amount of time. Surveys with the HST therefore focus on faint, distant galaxies, so we end up with images having similar quality to those from SDSS, which is remarkable given how much further away the HST galaxies are compared with those from SDSS.

The similarity between the images of galaxies in the early universe from HST and those relatively nearby from SDSS is actually a big advantage. It means that we can fairly compare the morphologies of galaxies at these two eras in the Universe’s history. That’s what professional astronomers will be doing with your Galaxy Zoo: Hubble classifications over the coming year.

Hubble, meet Galaxy Zoo. Galaxy Zoo, meet Hubble.

Regular blog readers will know that we were all hugely pleased to find out that our proposal to observe Hanny’s Voorwerp with Hubble was approved. This was  especially welcome because we expected a very high oversubscription rate for next year – new and repaired instruments meant that there was pent-up demand for some kinds of observations which have not been possible for several years. Nearly 1000 proposals were submitted to the Space Telescope Science Institute (STScI). which managed a complex review process involving about 200 astronomers from all over the world (noting that Hubble is a cooperative project of NASA and the European Space Agency). Specialized panels of reviewers looked at various subfields of astronomy, comparing the likely scientific fruitfulness of a wide range of projects.This last week saw the deadline for the next step in preparing for next year’s  Hubble observations – what’s known as Phase II. This uses software distributed by STScI to plan each operation in detail – every exposure, filter change, and minute telescope motion. The astronomer can find out whether reordering certain operations uses precious telescope time more efficiently, and whether the results can be improved by restricting the observations to certain orientations of the telescope or times of year. The software will also overlay requested fields of view on sky surveys such as Sloan images), a welcome reality check that you’ve told it to look in the right place. This stage also gives us a chance to see whether anything we’ve learned since the proposal was submitted in early March gave us reason to change any of our originally proposed measurements.

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