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Stellar Populations of Quiescent Barred Galaxies Paper Accepted!

A new paper using Galaxy Zoo 2 bar classification has recently been accepted!

In this paper (which can be found here:, we use hundreds of SDSS spectra to study the types of stars, i.e., stellar populations, that make up barred and unbarred galaxies. The reason for this study is that simulations predict that bars should affect the stellar populations of their host galaxies. And while there have been numerous studies that have addressed this issue, there still is no consensus.

A graphic summary of this study is shown here:


In this study, we stack hundreds of quiescent, i.e., non-star-forming, barred and unbarred galaxies in bins of redshift and stellar mass to produce extremely high-quality spectra. The center-left panel shows our parent sample in grey, and the cyan and green hash marks represent our galaxy selection for our bulge and gradient analysis. The black rectangle represents one of the bins we use. The upper and lower plots show the resultant stacked spectra of the barred and unbarred galaxies, respectively. We show images of barred and unbarred galaxies in the center, selected with the Galaxy Zoo 2 classifications. Finally, the center-right panel shows the ratio of these two stacked spectra at several wavelengths that reflect certain stellar population parameters.

Our main result is shown here:


We plot several stellar population parameters as a function of stellar mass for barred and unbarred galaxies. Specifically, we plot the stellar age, which gives us an idea of the average age of a galaxy’s stars, stellar metallicity ([Fe/H]), which gives us an idea of the relative amount of elements heavier than hydrogen in a galaxy, alpha-abundance ([Mg/Fe]), which gives us an idea of the timescale it took to form a galaxy’s stars, and nitrogen abundance ([N/Fe]), which also gives us an idea of the timescale it took to form a galaxy’s stars.

The main result of our study is that there are no statistically significant differences in the stellar populations of quiescent barred and unbarred galaxies. Our results suggest that bars are not a strong influence on the chemical evolution of quiescent galaxies, which seems to be at odds with the predictions.

Finished with Hubble (for now), with new images going back to our “local” Universe

Thanks for everyone’s help on the recent push with the Hubble CANDELS and GOODS images. I’m happy to say that we’ve just completed the full set, and are working hard on analysis of how the new depths change the morphologies. In the meantime, we’re delighted to announce that we have even more new images on Galaxy Zoo!

The new set of images now active are slightly different for us, and so we wanted to explain here what they are and why we want to collect classifications for them.

In all phases of Galaxy Zoo so far we have shown you galaxy images which are in colour. The details of how these are created varies depending on which survey the images are from. With the SDSS images, we combine information from three of the five observational filters used by Sloan (g, r, i) to produce a single three-colour image for each galaxy. We’ve talked before in more detail about how those colour images are made. All five Sloan filters and their wavelengths and sensitivity are shown below. You can probably see why we’d pick gri for our standard colour images: these are the most sensitive filters, roughly in the “green”, “red” and “infrared” (or just about) parts of the spectrum.

SDSS Filters

The five SDSS filters and the wavelengths they span.


Each of the SDSS filters is designed to observe the galaxy at a different part of the visible (or near visible) spectrum, with the bluest filter (the u-band; just into the UV part of the spectrum) and the reddest the z-band (which is into the infra-red). Different types of stars dominate the light from galaxies in different parts of the spectrum, for example hot massive young stars are very bright in the u-band, while dimmer lower mass stars are redder. Galaxies with older populations of stars will therefore look redder, as the massive blue stars will all have gone supernova already.

We are interested in measuring how a galaxy’s classification differs when it’s observed in each of the filters individually. To investigate this specific question, we have put together a selection of SDSS galaxies and instead of showing you a single three-colour image for each, we are showing you separately the original single filter images. We want you to classify them just as normal, and we will use these classifications to quantify how the classification changes from the blue to the red images.

Example postage stamp images of the monochromatic single filter images.

Example postage stamp images of the monochromatic single filter images.

Astronomers have a good “rule of thumb” for what should happen to galaxy morphology as we move to redder (or bluer) filters, but it’s only ever been measured in very small samples of galaxies. With your help we’ll make a better measurement of this effect, which will be really useful in the interpretation of other trends we observe with galaxy colour.

(Hint: some users might want to use the “Invert” button on the Galaxy Zoo interface a little bit more for these images, as some galaxies are more clearly seen when you toggle it.)

Explore Galaxy Zoo Classifications

This post (and visualization) is by Coleman Krawczyk, a Zooniverse Data Scientist at the ICG at the University of Portsmouth

Today we’ve added another new tool for visualizing Galaxy Zoo, this time showing the full vote path of all users for each galaxy from GZ2 onward.  The first node of the visualization shows an image of the galaxy and each of the other nodes represents the answer to a question from the Galaxy Zoo decision tree, and the size of the node is proportional to the number of votes for that answer.  The maximal vote path is highlighted and also shown in words across to top of the tree, and the results of the “Is there anything odd?” question are shown across the bottom.
The full Galaxy Zoo catalog can be searched via Zooniverse ID (the same one used for Talk), RA and Dec, or randomly.  After picking a galaxy the nodes can be moved around by clicking and dragging, and the links can be collapsed/expanded by clicking the attached nodes, both of these functions are useful for untangling complex trees.  Various properties of the visualization can also be controlled with the sliders below the tree.  For a guided tour of this tool click the “Take a tour” button, and for a full list of features click the “Help” button.
Screenshot of the Visualisation Tool

Screenshot of the Visualisation Tool

Visualizing the decision trees for Galaxy Zoo

This post (and visualization) is by Coleman Krawczyk, a Zooniverse Data Scientist at the ICG at the University of Portsmouth

Today we’ve added a new tool that visualizes the full decision tree for every Galaxy Zoo project from GZ2 onward (GZ1 only asked users one question, and would make for a boring visualization).  Each tree shows all the possible paths Galaxy Zoo users can take when classifying a galaxy.  Each “task” is color-coded by the minimum number of branches in the tree a classifier needs to take in order to reach that question.  In other words, it indicates how deeply buried in the tree a particular question is, a property that is helpful when scientists are analyzing the classifications.

Galaxy Zoo has used two basic templates for its decision trees.  The first template allowed users to classify galaxies into smooth, edge-on disks, or face on disks (with bars and/or spiral arms) and was used for Galaxy Zoo 2, the infrared UKIDSS images, and is currently being used for the SDSS data that is live on the site. The second template was designed for high-redshift galaxies, and allows users to classify galaxies into smooth, clumpy, edge on disks, or face on disks. This template was used for Galaxy Zoo: Hubble (GZ3), FERENGI (artificially redshifted images of galaxies), and is currently being used by the CANDELS and GOODS images in GZ4.  Although these final three projects ask the same basic questions, there are some subtle differences between them in the questions we ask about the bulge dominance, “odd” features, mergers, spiral arms, and/or clumps.

Visualization of the decision tree for Galaxy Zoo 2 (GZ2), by C. Krawcyzk. Colors indicate the depth of a particular question within the decision tree.

Visualization of the decision tree for Galaxy Zoo 2 (GZ2), by C. Krawczyk. Colors indicate the depth of a particular question within the tree.

If you ever wanted to know all the questions Galaxy Zoo could possibly ask you, head on over to the new visualization and have a look!

New paper: Galaxy Zoo and machine learning

I’m really happy to announce a new paper based on Galaxy Zoo data has just been accepted for publication. This one is different than many of our previous works; it focuses on the science of machine learning, and how we’re improving the ability of computers to identify galaxy morphologies after being trained off the classifications you’ve provided in Galaxy Zoo. This paper was led by Sander Dieleman, a PhD student at Ghent University in Belgium.

This work was begun in early 2014, when we ran an online competition through the Kaggle data platform called “The Galaxy Challenge”. The premise was fairly simple – we used the classifications provided by citizen scientists for the Galaxy Zoo 2 project and challenged computer scientists to write an algorithm to match those classifications as closely as possible. We provided about 75,000 anonymized images + classifications as a training set for participants, and kept the same amount of data secret; solutions submitted by competitors were tested on this set. More than 300 teams participated, and we awarded prizes to the top three scores. You can see more details on the competition site.

Since completing the competition, Sander has been working on writing up his solution as an academic paper, which has just been accepted to Monthly Notices of the Royal Astronomical Society (MNRAS). The method he’s developed relies on a technique known as a neural network; these are sets of algorithms (or statistical models) in which the parameters being fit can change as they learn, and can model “non-linear” relationships between the inputs. The name and design of many neural networks are inspired by similarities to the way that neurons function in the brain.

One of the innovative techniques in Sander’s work has been to use a model that makes use of the symmetry in the galaxy images. Consider the pictures of the same galaxy below:

Screen Shot 2015-03-27 at 4.16.07 PM

A galaxy from GZ2, shown both with no rotation (left) and rotated by 45 degrees (right).

From the classifications in GZ, we’d expect the answers for these two images to be identical; it’s the same galaxy, after all, no matter which way we look at it. For a computer program, however, these images would need to be separately analyzed and classified. Sander’s work exploits this in two ways:

  1. The size of the training data can be dramatically increased by including multiple, rotated versions of the different images. More training data typically results in a better-performing algorithm.
  2. Since the morphological classification for the two galaxies should be the same, we can apply the same feature detectors to the rotated images and thus share parameters in the model. This makes the model more general and improves the overall performance.

Once all of the training data is in, Sander’s model takes images and can provide very precise classifications of morphology. I think one of the neatest visualizations is this one: galaxies along the top vs bottom rows are considered “most dis-similar” by the maps in the model. You can see that it’s doing well by, for example, grouping all the loose spiral galaxies together and predicting that these are a distinct class from edge-on spirals.

From Figure 13 in Dieleman et al. (2015). Example sets of images that are maximally distinct in the prediction model. The top row consists of loose winding spirals, while the bottom row are edge-on disks.

From Figure 13 in Dieleman et al. (2015). Example sets of images that are maximally distinct in the prediction model. The top row consists of loose winding spirals, while the bottom row are edge-on disks.

For more details on Sander’s work, he has an excellent blog post on his own site that goes into many of the details, a lot of which is accessible even to a non-expert.

While there are a lot of applications for these sorts of algorithms, we’re particularly interested in how this will help us select future datasets for Galaxy Zoo and similar projects. For future surveys like LSST, which will contain many millions of images, we want to efficiently select the images where citizen scientists can contribute the most – either for their unusualness or for the possibility of more serendipitous discoveries. Your data are what make innovations like this possible, and we’re looking forward to seeing how these can be applied to new scientific problems.

Paper: Dieleman, Willett, & Jambre (2015). “Rotation-invariant convolutional neural networks for galaxy morphology prediction”, MNRAS, accepted.

New Images on Galaxy Zoo, Part 1

We’re delighted to announce that we have some new images on Galaxy Zoo for you to classify! There are two sets of new images:

1. Galaxies from the CANDELS survey

2. Galaxies from the GOODS survey

The general look of these images should be quite familiar to our regular classifiers, and we’ve already described them in many previous posts (examples: here, here, and here), so they may not need too much explanation. The only difference for these new images are their sensitivities: the GOODS images are made from more HST orbits and are deeper, so you should be able to better see details in a larger number of galaxies compared to HST.

Comparison of the different sets of images from the GOODS survey taken with the Hubble Space Telescope. The left shows shallower images from GZH with only 2 sets of exposures; the right shows the new, deeper images with 5 sets of exposures now being classified.

Comparison of the different sets of images from the GOODS survey taken with the Hubble Space Telescope. The left shows shallower images from GZH with only 2 sets of exposures; the right shows the new, deeper images with 5 sets of exposures now being classified.

The new CANDELS images, however, are slightly shallower than before. The main reason that these are being included is to help us get data measuring the effect of brightness and imaging depth for your crowdsourced classifications. While they aren’t always as visually stunning as nearby SDSS or HST images, getting accurate data is really crucial for the science we want to do on high-redshift objects, and so we hope you’ll give the new images your best efforts.

Images from the CANDELS survey with the Hubble Space Telescope. Left: deeper 5-epoch images already classified in GZ. Right: the shallower 2-epoch images now being classified.

Images from the CANDELS survey with the Hubble Space Telescope. Left: deeper 5-epoch images already classified in GZ. Right: the shallower 2-epoch images now being classified.

Both of these datasets are relatively small compared to the full Sloan Digital Sky Survey (SDSS) and Hubble Space Telescope (HST) sets that users have helped us with over the last several years. With about 13,000 total images, we hope that they’ll can be finished by the Galaxy Zoo community within a couple months. We already have more sets of data prepared for as soon as these finish – stay tuned for Part 2 coming up shortly!

As always, thanks to everyone for their help – please ask the scientists or moderators here or on Talk if you have any questions!

Radio Galaxy Zoo searches for Hybrid Morphology Radio galaxies (HyMoRS): #hybrid

First science paper on hybrid morphology radio galaxies found through Radio Galaxy Zoo project has now been submitted!

hybrid_blogfig1 In the paper we have revised the definition of the hybrid morphology radio galaxy (HyMoRS or hybrids) class. In general, HyMoRS show different Fanaroff-Riley radio morphology on either side of the active nucleus, that is FRI type on one side and FRII on the other side of their infrared host galaxy. But we found that this wasn’t very precise, and set up a clear definition of these sources, which is:

”To minimise the misclassification of HyMoRS, we attempt to tighten the original morphological classification of radio galaxies in the scope of detailed observational and analytical/numerical studies undertaken in the past 30 years. We consider a radio source to be a HyMoRS only if

(i) it has a well-defined hotspot on one side and a clear FR I type jet on the other, though we note the hotspots may `flicker’, that is their brightness may be rapidly variable (Saxton et al. 2002), and, in the case the radio source has a very prominent core or is highly asymmetric,

(ii) its core prominence does not suggest strong relativistic beaming nor its asymmetric radio structure can be explained by differential light travel time effects. ”

Based on this we revised hybrids reported in scientific literature and found 18 objects that satisfy our criteria. With Radio Galaxy Zoo during the first year of its operation, through our fantastic RadioTalk, you guys now nearly doubled this number finding another 14 hybrids, which we now confirm! Two examples from the paper are below:

We also looked at the mid-infrared colours of hybrids’ hosts. As explained by Ivy in our last RGZ blog post (, the mid-infrared colour space is defined by the WISE filter bands: W1, W2 and W3, corresponding to 3.4, 4.6 and 12 microns, respectively.

The results are below:


For those of you interested in seeing the full paper, we will post a link to freely accessible copy once the paper is accepted by the journal and is in press! :)

Fantastic job everyone!
Anna & the RGZ science team

First Radio Galaxy Zoo paper has been submitted!

The project description and early science paper (results from Year 1) for the Radio Galaxy Zoo project has been submitted!


authorlist1We find that the RGZ citizen scientists are as effective as the science experts at identifying the radio sources and their host galaxies.

Based upon our results from 1 year of operation, we find the RGZ host galaxies reside in 3 primary loci of mid-infrared colour space.  The mid-infrared colour space is defined by the WISE filter bands: W1, W2 and W3, corresponding to 3.4, 4.6 and 12 microns; respectively.

Approximately 10% of the RGZ sample reside in the mid-IR colour space dominated by elliptical galaxies, which have older stellar populations and are less dusty, hence resulting in bluer (W2-W3) colours. The 2nd locus (where ~15% of RGZ sources are found) lies in the colour space known as the `AGN wedge’, typically associated with X-ray-bright QSOs and Seyferts. And lastly, the largest concentration of RGZ host galaxies (~30%) can be found in the 3rd locus usually associated with luminous infrared galaxies (LIRGs).  It should be noted that only a small fraction of LIRGs are associated with late-stage mergers.  The remainder of the RGZ host population are distributed along the loci of both star-forming and active galaxies, indicative of radio emission from star-forming galaxies and/or dusty elliptical (non-star-forming) galaxies. See the figure below for a plot of these results.

blog_fig2Caption to figure WISE colour-colour diagram, showing sources from the WISE all-sky catalog (colourmap), 33,127 sources from the 75% RGZ catalog (black contours), and powerful radio galaxies (green points) from (Gürkan et al. 2014). The wedge used to identify IR colours of X-ray-bright AGN from Lacy et al. (2004) & Mateos et al. (2012) is overplotted (red dashes). Only 10% of the WISE all-sky sources have colours in the X-ray bright AGN wedge; this is contrasted with 40% of RGZ and 49% of the Gürkan et al. (2014) radio galaxies. The remaining RGZ sources have WISE colours consistent with distinct populations of elliptical galaxies and LIRGs, with smaller numbers of spiral galaxies and starbursts.

In addition, we will also be submitting our paper on Hybrid Morphology Radio Sources (HyMoRS) in the next few days so stay tuned!

As always, thank you all very much for all your help and support and keep up the awesome work!

Julie, Ivy & the RGZ science team

Zooniverse at Mauna Kea, Day 6: This is the End

Ed, Chris, Sandor, and Becky in front of the telescope

Part 1, Part 2, Part 3, Part 4, Part 5

I’m not sure if we’ve been especially unlucky or if this is the norm for observing trips, but we once again the weather is curtailing our telescope time. After a few hours of normal observing, clouds started to blow across the top of Mauna Kea, and now it’s raining outside the dome.

Tonight's Weather

The Dip in the humidity (2nd from the top) represents when we were able to observe.

In the meantime, Becky and I shot a short video tour of the dome a couple days ago you can check out:

Tomorrow, we check out of Hale Pohaku and head down to Hilo for a night. Then I’m off to Chicago and Becky and Sandor are back to Oxford. Even with the bad weather, sleep deprivation, and static electricity, this trip has been a really great experience for me. I now know infinitely more about radio astronomy than I did before! I hope the people doing the real work were able to get all the data they needed.

A Few Notes:

Sad Becky

This sums up the general mood

  • Sandor and Becky took some sick photos around sunset, you should check out all of them.
  • When everything is terrible, you just have to let it go.
  • Thanks again to all the Galaxy Zoo volunteers, whose work made this observing trip possible for us. You are the best.

Zooniverse at Mauna Kea, Day 5: The Wind Strikes Back


Part 1, Part 2, Part 3, Part 4

After few good days of observations the wind has returned to ruin our fun. The CSO telescope is supposed to be closed when the wind is above 35mph. Curiously the telescope itself doesn’t have its own anemometer, so we have to rely on readings from the other telescopes on the mountain to decide if it is safe to open the telescope building.

Feeling this entire situation was quite unsatisfactory, I decided to build my own anemometer using a clipboard with a ruler and Becky’s boot, giving you the answer to Chris’s question from earlier tonight:

Graph of Wind speed vs Deflection Angle

Shout out to Mrs Beck’s AP Physics for me understanding this

Using the above chart we tried to workout the wind speed. We had to do a bit of fudging. We decided the boot was a perfect cylinder (drag coefficient 0.82), and that it weighed about 300g. We also decided not to take into account lower air pressure. Finally when Sandor and I calculated it independently, we got wildly different results, so it was a futile exercise in the end. (Also CSO buy an anemometer)

Sandor doing the hard work!

Sandor doing the hard work!

Since then, we’ve been playing chicken with the wind. Sometimes having to close the dome. Sometimes thinking we can be open, only to have the telescope struggle to stay on target. Sometimes we hear Meg Schwamb‘s wind tracker say “Warning High Winds”.  The conditions made us miss out on a second night of observing Comet Lovejoy, and everyone seemed pretty down for most of the night.

Around 1 or 2am the wind finally let up and we were able to start observing, so the night wasn’t a complete loss. Hopefully the weather tomorrow is better.

A Few Notes:

  • It’s really hard to get enough sleep. Sleeping at altitude is hard anyway, and adding in trying to sleep during the day gives us all points for degree of difficulty. Everyone has lovely bags around their eyes.
  • This is the last day Chris is with us. We’ll be all alone tomorrow night.
  • Sandor is succumbing to the static curse now too.
  • @GeertHub on Twitter wanted to me to post a screen shot of the telescope software: snapshot1
  • All the Sex & Drugs & Rock & Roll is helping us touch the sky.

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