Press Release on Results from Galaxy Zoo: 3D

Many of you helped out with the Galaxy Zoo spinoff project, Galaxy Zoo: 3D. I am happy to let you know that I am presenting results from this project, today at the 237th Meeting of the American Astronomical Society. You can view the iPoster I made about it at this link.

This spin-off project was aimed at supporting the MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) survey, which is part of the Sloan Digital Sky Surveys (SDSS). Thanks to your input we have been able to crowdsource maps which show where the spiral arms, bars and any foreground stars are present in every galaxy observed by MaNGA. This, combined with the MaNGA data is helping to reveal how these internal structures impact galaxies.

The results will be part of a Press Conference about this and other SDSS results, live streamed at 4.30pm ET (9.30pm GMT) on the AAS Press Office Youtube Channel. The press release about them will go live on the SDSS Press Page at the same time. Direct link to press release (will only work after 4.30pm ET).

Thanks again for your contributions to understanding how galaxies work.

A sad farewell

I recently received word from his wife of the death of Jean Tate on November 6. Jean had been a very active participant in several astronomical Zooniverse projects for a decade, beginning with Galaxy Zoo. It does no disservice to other participants to note that he was one of the people who could be called super-volunteers, carrying his participation in both organized programs and personal research to the level associated with professional scientists. He identified a set of supergiant spiral galaxies, in work which was, while in progress, only partially scooped by a professional team elsewhere, and was a noted participant in the Andromeda project census of star clusters in that galaxy. In Radio Galaxy Zoo, he was a major factor in the identification of galaxies with strong emission lines and likely giant ionized clouds (“RGZ Green”), and took the lead in finding and characterizing the very rare active galactic nuclei with giant double radio sources from a spiral galaxy (“SDRAGNs”). He did a third of the work collecting public input and selecting targets to be observed in the Gems of the Galaxy Zoos Hubble program. Several of us hope to make sure that as much as possible of his research results from these programs are published in full.

Jean consistently pushed the science team to do our best and most rigorous work. He taught himself to use some of the software tools normally employed by professional astronomers, and was a full colleague in some of the Galaxy Zoo research projects. His interests had been honed by over two decades of participation in online forum discussions in the Bad Astronomy Bulletin Board (later BAUT, then Cosmoquest forum), where his clarity of logic and range of knowledge were the bane of posters defending poorly conceived ideas.

Perhaps as a result of previous experiences as a forum moderator, Jean was unusually dedicated to as much privacy as one can preserve while being active in online fora and projects (to the point that many colleagues were unaware of his gender until now). This led to subterfuges such as being listed in NASA proposals as part of the Oxford astronomy department, on the theory that it was the nominal home of Galaxy Zoo. Jean was married for 27 years, and had family scattered in both hemispheres with whom he enjoyed fairly recent visits. Mentions in email over the years had made me aware that he had a protracted struggle with cancer, to the extent that someday his case may be eventually identifiable in medical research. He tracked his mental processes, knowing how to time research tasks in the chemotherapy cycle to use his best days for various kinds of thinking.

This last month, emails had gone unanswered long enough that some of us were beginning to worry, and the worst was eventually confirmed. I felt this again two days ago, which was the first time I did not forward notice of an upcoming Zoo Gems observation by Hubble to Jean to be sure our records matched.

Ad astra, Jean.

Radio Galaxy Zoo: LOFAR – A short update

A lot has happened on the Radio Galaxy Zoo since we last posted an update!

First of all, you can see on the image above that we are making great progress with getting all of the big, bright sources from the LOFAR survey looked at by Zooniverse volunteers. We are approaching half a million classifications and just under 80,000 radio sources have been looked at by at least five volunteers at the time of writing. Together with the earlier efforts by members of the LOFAR team, we have covered a very wide area of the sky, around 3,000 square degrees, which is well over half of the area of the LOFAR data, and are well on the way to completing the original aims of the project. The green, orange and pink areas together show the areas of the sky we have completed.

What’s next? One of the key goals of the LOFAR Radio Galaxy Zoo has always been to provide targets for the WEAVE-LOFAR spectroscopic survey. WEAVE is a new spectroscope being commissioned on the William Herschel Telescope, which can measure 1,000 redshifts of galaxies in a single observation. WEAVE-LOFAR aims to find the redshifts of every bright LOFAR source in the survey. But the survey can’t work without knowing where the optical host galaxies are — so the input of Zooniverse volunteers in selecting these host galaxies is absolutely crucial to our success.

A complication is that WEAVE wants to look at all LOFAR sources, not just the large ones that we generally select for the Zooniverse project. As regular users will know, there are many small sources in the radio sky as well, and the optical counterparts of those can be found automatically just by matching with optical catalogues. In between there are some intermediate-sized sources, and these present the biggest problem; some of them benefit from viewing by volunteers, but there are too many of them for us to look at them all. Earlier in the year we selected 10,000 of these in a particular region of the sky that we thought would benefit from human inspection using a combination of algorithms and machine learning, and injected them into the Zooniverse project to see what volunteers made of them. The results are encouraging and have allowed us to develop a process of ‘early retirement’ for sources that turn out not to be interesting (i.e. no clicks are made during classification). Our next priority is to select this type of source, informed by the first set of results, over a larger area of the sky in order to get the full set of inputs for the first year of WEAVE. You’ll see these sources entering the Zooniverse project over the coming weeks.

Presenting results from the Galaxy Builder project

From April 2018 until early this year, Galaxy Builder has collected over 18,000 models of spiral galaxies, built by volunteers. These models were combined and computationally fine-tuned, and the results have been compiled into Lingard et al. 2020 recently accepted for publication in the Astrophysical Journal.

The project asked volunteers to sequentially add components to a galaxy, starting with the galaxy’s disc, then, if one is present, a bulge and a bar, followed by tracing any visible spiral arms. At each stage, light from the corresponding component would be removed until the whole galaxy was accounted for:

Four-panel figure showing the galaxy builder interface, a spiral galaxy is visible in blue, and in each panel another component is added to gradually remove all the visible light from the galaxy.

Four-panel figure showing the galaxy builder interface, a spiral galaxy is visible in blue, and in each panel another component is added to gradually remove all the visible light from the galaxy.


After collecting 30 volunteer models for each galaxy, we then used Machine Learning techniques to cluster components, and identify a “consensus model”.

Four panel plots showing the clusterd and consensus components for an example galaxy. There is a small amount of scatter in each component, but the clustering has reliably found a good result.

Four panel plots showing the clusterd and consensus components for an example galaxy. There is a small amount of scatter in each component, but the clustering has reliably found a good result.

We then used a computer fitting algorithm to fine-tune this model, resulting in a detailed description of the galaxy’s light distribution, which we can use to understand the physical processes occurring inside it!

Five panel plot showing an image of the example galaxy, the fitted model (which matches the real galaxy very well), the difference between the galaxy and model (which is small), and how the consensus components from clustering have changed during fitting (they do not change very much)

Five panel plot showing an image of the example galaxy, the fitted model (which matches the real galaxy very well), the difference between the galaxy and model (which is small), and how the consensus components from clustering have changed during fitting (they do not change very much)

We have shown that galaxy model created in this way are just as reliable as simpler models obtained purely through computer fitting (when those simple models are appropriate!), by comparing to other published work and by incorporating a small sample of synthetic galaxies, for which we know the true light profiles:

The nine synthetic galaxy images, each of which look very realistic (but without clumpy star-forming regions). Most have spiral arms, and some have bars.

The nine synthetic galaxy images, each of which look very realistic (but without clumpy star-forming regions). Most have spiral arms, and some have bars.

For most parameters, the difference between the true (x-axis) and volunteer-provided (y-axis) values is tiny. There are some issues with bar “boxyness” and bulge concentration (Sérsic index), primarily due to the computer fitting algorithm not being able to distinguish between different combinations of values:

Scatter plots showing how well parameters are recovered. We see that the method generally does a very good job, but there is a lot of scatter in bulge sersic index and bar boxyness.

Scatter plots showing how well parameters are recovered. We see that the method generally does a very good job, but there is a lot of scatter in bulge sersic index and bar boxyness.

Thanks to the added complexity of Galaxy Builder models, we have a much richer source of information for scientists to delve into! We’re excited to share the scientific results we’ve obtained, expect another post in the not-too-distant future (hint, spiral arms are complicated)!

The research team want to send a very warm thank-you to everyone who has participated in this project over the years. Without your efforts we would not have had the chance to do the science we are so passionate about, and we are very excited for the future of the Zooniverse.

Galaxy Builder is currently finished collected classifications, but we still need your classifications in Galaxy Zoo, where we’re working on collecting classifications for images from the DECaLs survey.

This blog was posted on behalf of Tim Lingard for the Galaxy Builder Team. Tim also submitted his PhD thesis based on this work this summer, and is now working as a Data Analyst for the 1715 Labs

Galaxy Zoo: Clump Scout – a first look at the results

Hi all! Nico here, grad student from the Minnesota science team, with an update on the Galaxy Zoo: Clump Scout project.

Since launching Clump Scout in September of last year, we’ve had over 7,000 volunteers provide more than 800,000 classifications! We’re incredibly grateful for your help and we’ve been excitedly exploring the data as it has been coming in to learn more about clumpy galaxies in the local universe.

Now that we’re around the halfway point with this project, we wanted to share with you some of the things we’ve learned. If you’d like a refresher on the project, you can see our original “project launch” blog post here.

A few things we’ve learned so far…

We’ve found a set of local clumpy galaxies to examine more closely.

HST follow-up sample  Figure 1: A small sample of clumpy galaxies near us. These are some of the galaxies for which we’ve requested follow-up observations by the Hubble Space Telescope.

A major goal of the Clump Scout project was to find a group of local galaxies that were “clumpy”. For the time we’ve known about clumpy galaxies, they’ve mostly been considered a “high-redshift” phenomenon — which is astronomy-speak for “very far away, and very long ago”. In fact, we first discovered clumpy galaxies by examining images of the very distant universe taken by the Hubble Space Telescope. Because these galaxies were so far away, their light took billions of years to reach us, and we were seeing them as they existed when the universe was only a fraction of the age that it is now. It quickly became clear that most early-universe galaxies did not look like local galaxies, and the “spiral” or “elliptical” structure that we’re used to seeing was mostly absent. Instead, most galaxies were loosely-structured blobs of stars and gas with a few concentrated “clumps” that glowed brightly with new stars. The name “clumpy galaxy” originated to explain the appearance of these galaxies, and to differentiate them from the appearance of galaxies near us.

Unfortunately, because these galaxies are so distant, it’s difficult to study them in detail. We have wondered over the years if there are properties of clumps that are being hidden or washed-out by the dim, low-resolution photos we’ve taken from billions of light-years away. This is why the discovery of clumpy galaxies in our own backyard is such an exciting accomplishment. Thanks to the volunteer classifications from the Clump Scout project, we’ve been able to identify hundreds of galaxies with clumpy characteristics much like the much more distant versions we’re used to studying — but since they are nearby, we can perform follow-up studies with more sensitive, higher-resolution techniques. We recently submitted a proposal for observation time from the Hubble Space Telescope to examine some of these galaxies in more detail, and we’re excitedly waiting to hear back. Above, you can see ten of the galaxies for which we requested follow-up. They are dotted with blue specks, which are the “clumps” we’ve been seeking to study.

It’s much harder to see clumps in some places than others.

The Galaxy Zoo team has run many projects that study the large-scale properties of galaxies, such as their shape, characteristics, and patterns in their behavior. The Clump Scout project is a bit different because our focus is on a much smaller target. Clumps are small “substructures” within galaxies, which are much harder to see and in many cases can be entirely missed.

Part of our job during this project was to determine the properties of clumps that our volunteers could see compared to the properties of those that they couldn’t. For example, a bright clump in a dim galaxy sticks out like a sore thumb; a dim clump in a bright galaxy, on the other hand, might be completely invisible. To control this effect, we created a sample of simulated clumps with properties we already knew well, and inserted these into some galaxy images in the project. Now that so many volunteers have responded, we have a good idea of which simulated clumps can be seen and which cannot — which gives us a very good idea of what sorts of real clumps might be missing as well.

The main factor controlling whether or not a clump is visible is, of course, how bright it is. You, our volunteers, have shown us that you can catch just about all of the clumps that are above the “95% completeness limit” of the Sloan Digital Sky Survey (SDSS), the survey which provides all of Clump Scout’s images. Essentially, this means that if a clump CAN be found, you all are finding it!

Other factors controlling clump visibility were more surprising. For example, we expected that the higher an image’s resolution, the easier it would be to see clumps. In fact, resolution appeared to have almost NO effect on volunteers’ ability to see clumps: Volunteers recovered the same fraction of clumps in the clearest images as in the blurriest ones. Aside from the clump’s brightness, the most important factor in clump recovery was actually its proximity to the center of its host galaxy. We found that clumps in the dimmer, more outlying regions of galaxies are quite easy to see — they are bright spots on a dim background. However, once they are within one “effective radius” of the galactic center, they become incredibly difficult to identify. This makes sense: The galactic center is much brighter and may drown out the signal of a clump near it. This gives us a very helpful tool for understanding the patterns in clumps we are seeing. Many theories about clumps predict that they live for billions of years, beginning near the outside edges of their host galaxies and slowly migrating inward towards the center before merging with the central bulge. We now know that we are not likely to see clumps near the central bulge in our Clump Scout data, but it’s not necessarily because they’re not there: They are merely harder to see.

Recovery fractions

Figure 2: The “recovery curves” for clumps in our sample. On each plot, the height of the blue region measures the number of simulated clumps with a given property, while the orange region’s height measures the number of those simulated clumps that volunteers found and marked. The ratio between these two is called the “recovery fraction”, and it’s displayed as the black line on the plot. The recovery fraction doesn’t change much with redshift (aka distance to the galaxy) or with image resolution. However, it falls dramatically as clumps get closer to the galactic center — which tells us exactly how much harder it is to find clumps that are near the center of a galaxy.

We’re still working through our analysis of your responses, and we’ll continue to give you updates as they come. Thank you for being part of the Galaxy Zoo team!

If you’d like to try your hand at identifying a few clumps yourself, you can take part at our project page: Galaxy Zoo: Clump Scout 




Radio Galaxy Zoo: LOFAR – The First Classification Results

Presenting some results from the first two weeks of the Radio Galaxy Zoo: LOFAR project.

Hi everyone! We are extremely excited to see how popular the Radio Galaxy Zoo: LOFAR project is. Since we launched two weeks ago we’ve already had over 234,000 classifications! In this brief blogpost we’d like to give an overview about the classification statistics and how the project is coming along. The in-depth scientific results will follow later, after more careful analysis.

Some General Statistics


In the graph above, you can see the number of classifications per hour. The graph starts at the launch date of the project (25/02) as you can see from the smaller first peak, we started at about 500 classifications an hour. Things really start taking off rapidly around the morning of the next day, as the European press released their articles about the Radio Galaxy Zoo: LOFAR, peaking at 3000 classifications an hour! Afterwards, we see the generally expected day and night trends, following European time, thus indicating most volunteers are European.


The figure above shows the number of classifications grouped per language setting. As is very clear, English is the dominant one, almost 80% of the classifications are made through the English version of the website. However, French is also a pretty popular langauge setting. This is just a proxy for the distribution of the countries however. It is a bit of a skewed view since there are probably many users that prefer to view the website in English, even when that is not their native language. This is the most likely explanation for the low number of classifications using Dutch language settings. You would expect a lot of classifications with the Dutch settings as the LOFAR telescope itself is located primarily in the Netherlands and therefore has gotten more attention from the Dutch press.


We’d also like to show the distribution of the number of classifications per user. When we zoom out (the right figure) we can see that there are a few users competing hard for the most classifications, right now there is a clear number one at more than 6,000 classifications already, which is amazing.

Interesting Sources

As of the time of writing this blogpost, we have already found a ton of interesting sources. Many very nice examples of classical double lobes, but also many complex cases and beautiful starforming galaxies have been identified already. As the project has just started, we have not had time to analyze the sources in detail yet, but stay tuned for updates on this!


Common Pitfalls

An interesting thing we noticed was that many people found ‘explosions’ in the Radio Galaxy Zoo: LOFAR, like the one in the image below. Unfortunately, these radial spokes are not an explosion but just imaging artefacts from where our calibration fails, which usually happens around very bright sources. If you see something like this, please click on “Artefact” at the final “Additional information” task.


Additionally, (real) diffuse emission is often mistaken for artefacts, but emission that is not mapping any compact structure is not necessarily an artefact, like the image below:


On the other hand, the small islands of emission in the image below are actually artefacts. Watch out!


Finally, some double lobes were also identified as blends, but this is likely just by volunteers that are still getting the hang of it. See the image below for two example cases that were incorrectly identified as a blend (three and four out of five times respectively). However, these are both just classic double-lobed radio galaxies.


Of course, this is just nitpicking on the cases that are going wrong, but most of the cases seem to be going well. Many real blended sources have also been identified, such as the examples below! So the option “Blend” should be picked when two distinct radio sources are under the solid ellipse.


How much sky have you covered so far?

The progress of the first two weeks of the Radio Galaxy Zoo: LOFAR has been amazing. In terms of sky area, the citizen scientists have already seen a quite a big chunk of the northern sky that we want to cover. The image below shows the sky area that we are currently investigating in light blue (called DR2, for data release 2). The purple and orange dots show the fields that the LOFAR team has done internally for the first data release (DR1) and for a small part of the second data release, respectively. The green dots show the fields that the public Radio Galaxy Zoo: LOFAR project has completed thus far.

You can see that in just two weeks we’ve already more than doubled the amount of area that took months for scientists to look at during the first data release of the LOFAR survey! If we keep up the current pace, we will be finished in no time.rgz_fig.png

As the project continues, we plan to give you more updates on the data reduction process, so stay tuned!

New Paper: Morphological Conformity in Galaxy Zoo

I’m delighted to report on the publication of the below paper, led by Justin Otter, a talented recent graduate of Haverford College who worked with me on this project since their Junior year (September 2017). Link to MNRAS abstract. Link to Arxiv version of accepted paper.


In this paper we used data from Galaxy Zoo 2 to investigate the idea of galactic “conformity” in galaxy morphologies.

What is Galactic Conformity?

Great question. It’s a fairly simple idea actually – the idea that satellite galaxies in groups are more likely to have similar properties to their central galaxy than a random galaxy in the Universe would. The satellites “conform” to the properties of the largest (central) galaxy in the group. So in a group with a star-forming central galaxy would be more likely to have star-forming satellite galaxies than a group with a passive (not star-forming) central galaxy.

What’s New in this Paper? 

The new thing here is looking at the star-formation and morphological properties of galaxies separately. So this comes right back to our discovery with the help of all of you at Galaxy Zoo (almost 10 years ago now!) that not all spirals are star-forming (blue), and not all ellipticals are passive (red).

Credit: Otter et al. 2020 (courtesy KLM)

Most studies looking into galactic conformity prior to our work had used star-formation properties to find the signal, and then interpreted the result as if it was a morphological change in the galaxies. This is obviously an over-simplification, and we (well actually it was Brooke who said it first!) wondered if this was driven by star-formation conformity or morphological conformity – and could you find the signal separately for the two.

So what did you find? 

Well we found conformity in both star formation and morphological type, but that the signal was stronger in star-formation properties. We also looked at satellites around red/passive and blue/star-forming spiral and elliptical central galaxies separately, and found the signal was largest around the red/passive elliptical central galaxies.

We made the following cartoon version of what we observed (note this is highly exaggerated – in all cases it was a small excess probability, not that all satellites around a central share its properties). All shapes of galaxies showed star-forming conformity, but there was only morphological conformity around ellipticals and blue spirals, suggesting that satellites of red spirals may have turned into ellipticals before the central galaxy

Credit: Justin Otter. A cartoon version of our observations

What does this mean for galaxy evolution? 

This all fits quite nicely into the picture where star-formation properties of galaxies can change quite easily, but it’s much harder to change morphology, so as a group evolves and/or accretes more satellites they are likely to change star-forming properties more easilly/quickly than the morphological properties.

So thanks again for the classifications which give us the data to do this work!

In case you’re curious, Justin is currently working as a Fulbright Scholar at the Max Planck Institute for Astronomy in Heidelberg, Germany, and will return to the US for the next academic year to begin his PhD at John’s Hopkins University.

Strong and weak bars in Galaxy Zoo

Good morning everyone,

My name is Tobias and I’m a new PhD student here at Oxford. I use the classifications everyone made in Galaxy Zoo to attempt to understand how galaxies evolve. Right now, I’m especially interested how bars affect galaxy evolution.

As some of you know, Galaxy Zoo currently asks to differentiate between so-called ‘strong’ or ‘weak’ bars. Below you can find some neat examples of both classes of galaxies that were identified using your classifications. It seems that the difference between strong and weak bars is some sort of combination between the length, width and brightness of the bar. 

Examples of strongly barred (top row) and weakly barred (bottom row) galaxies.

The relationship between bars and galaxy evolution has been studied before by members of the Galaxy Zoo team, but the previous incarnation of Galaxy Zoo only allowed binary answers to the bar question: either there was a bar or not. The interesting bit, however, is to see whether strong and weak bars have different effects.

In fact, we have exciting preliminary data that suggests both types do behave differently in the context of galaxy evolution! When a galaxy evolves and moves from the ‘blue cloud’ to the ‘red sequence’ in the colour-magnitude diagram, its morphology and properties change (e.g.: its star formation rate decreases). This process is called ‘galaxy quenching’. With the new Galaxy Zoo data and the classifications that everyone involved made, we saw that galaxies with weak bars are found in both the blue cloud and the red sequence, whereas the strongly barred galaxies are very much clustered in the red sequence, as you can see below. In more detail, strongly barred galaxies only make up ~5% of the blue cloud, while making up ~16% of the red sequence. To contrast this, weakly barred galaxies have a much more modest increase, populating ~17% and ~21% of the blue cloud and red sequence, respectively.

Contour plot of the colour-magnitude diagram for all the galaxies in Galaxy Zoo. Overlaid on top are the strongly barred galaxies (in green) and the weakly barred galaxies (in orange). The dotted line (taken from Masters et al. (2010)) defines ‘the blue edge of the red sequence’ and effectively divides the sample in two populations: the blue cloud and red sequence. One can clearly see that the strong bars are mainly above the dotted line.

This finding hints at a fundamental difference between the two types of bars, but in order to do real science we need to interpret the clustering of the strong bars correctly. Do strong bars cause the galaxy to quench and move up the red sequence or can a strong bar only form if the galaxy is already sufficiently quenched – a chicken or egg question on the scale of galaxies.

Before I end this post, I want to emphasise that this research is only made possible because of many volunteers, like yourself, that help classify galaxies and we are very grateful for your time and effort. However, this is only the start and a lot of work still needs to be done, so keep on classifying!

I hope to report on interesting new developments soon.


Galaxy Zoo Human + AI Paper Published

Hi all, Mike here.

A few months back, I introduced our new AI that can work together with volunteers to classify galaxies. It’s able to understand which galaxies, if classified by you, would best help it to learn. You and the AI have together classified tens of thousands of galaxies since we launched the new system in May.

I’m really happy to say that our paper was recently accepted for publication in the Monthly Notices of the Royal Astronomical Society!

We’ve made a few changes since the early version I shared before. I think the most interesting change is a new section applying AI fairness tools. These tools are usually used to check if AI models make biased decisions – for example, offering less jobs to women. We used these tools to check if our model is biased against galaxies with certain physical properties (it isn’t).

You can read the latest pre-print of the paper for free here. The (essentially identical) final publication will be also available for free from Monthly Notices once published – we’ll update this post when that happens.

Happy classifying,


The clumpiness of EAGLE galaxies

We have added new galaxies from the EAGLE simulations for you to classify on To find out more about what to do if some of them appear clumpy read this blog post.

It’s important to note that while EAGLE produces some impressive galaxy images, there are still some ways in which they don’t quite resemble real galaxies. A prominent example of this is in how many star-formation “clumps” there are in galaxies. Stars form in clumps or clusters of varying size, and some observed galaxies are clumpy in appearance, so the models are reproducing a real phenomenon. It also seems that these galaxies are more common in the early Universe, and are an important part of galaxy evolution. However, the clumpy galaxies may be too common within EAGLE.


Some EAGLE galaxies that appear clumpy. Clumps appear bright blue, because they have formed recently and contain the hottest and brightest (but shortest-lived) stars. From left to right, you can see clumpy galaxies that may appear disk-like, rounded or more chaotic in shape.

We have an understanding of why this happens: clumps can result from the limited detail with which galaxies can be modelled (even in the most powerful supercomputers), and the simplifications that need to be made to how gas interacts. This doesn’t affect other things we can learn from classifying these images. If you come across a galaxy that looks super-clumpy like the above images, the best thing to do is just ignore the clumpiness and classify the rest of the galaxy (If you would like to learn more about clumps, read about our sister project Galaxy Zoo: Clump Scout).