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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.
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:
After collecting 30 volunteer models for each galaxy, we then used Machine Learning techniques to cluster components, and identify a “consensus model”.
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!
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:
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:
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.
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.
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.
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
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.
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!
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.
As the project continues, we plan to give you more updates on the data reduction process, so stay tuned!
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).
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
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.
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.
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.
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.
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.
We have added new galaxies from the EAGLE simulations for you to classify on http://www.galaxyzoo.org. 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.
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).
We have added new galaxies from the EAGLE simulations for you to classify on http://www.galaxyzoo.org. To find out more about why we need your help with this task please read this blog post.
Modern telescopes allow us to marvel at the diverse galaxies scattered through the vast expanse of space. Each galaxy appears unique, but many share common features with others billions of light years away. These stunning images pose some fundamental questions. How did these galaxies come to be? What will happen to them? What does their appearance tell us about their past? We know it takes a very long time to build a galaxy; most of the nearby galaxies have been evolving for over 10 billion years. While galaxy evolution is exciting, we can hardly sit and wait for a galaxy to evolve in front of our eyes! Instead, the remarkably realistic simulated universes that are now being generated with modern supercomputers could hold the key to answering some of these questions. We are excited to announce a new image set of simulated galaxies from the EAGLE project. With your help, this will let us track how individual galaxies take their shape in a sophisticated simulated universe.
Computer models are increasingly powerful tools in astronomy, providing a tantalising glimpse into how galaxies evolve. We can follow the formation of galaxies in a simulated universe, once we include relevant processes such as the formation of stars, the growth of black holes and supernova explosions. The EAGLE project is a modern example of this, produced by a large international collaboration. EAGLE was run on a supercomputer using 4000 computer processors simultaneously over 4 months to generate a model universe. EAGLE is one of the most detailed model universes to date, and, along with the Illustris project, represents a historical advance in understanding various aspects of galaxy formation theory. This allows us to go through the 14 billion year history of the Universe in record time; from minuscule variations in the temperature of the first light of the Universe, to the emergence of the galaxies we see today. These detailed simulated galaxies have complex structure, particularly for galaxies as massive as our Milky Way. In EAGLE we can follow each galaxy’s complex family tree, providing a model for the direct evolution of individual galaxies. The EAGLE researchers ‘light up’ these simulated galaxies by modelling how stars shine, and how their light is obscured by dust.
To enable these simulated galaxy images to tell us more about the galaxies in our real Universe, we can harness the power of Galaxy Zoo. Collecting Galaxy Zoo classifications of the EAGLE galaxies will help our understanding of how the physical properties of galaxies translate to what we see through our telescopes. What’s more, by classifying simulated galaxies at different stages of their lives, we get an idea of how each galaxy took its shape, and insights into what physical processes are working behind the scenes. Could an unassuming elliptical galaxy be the faded remnant of a once grand spiral? Or even a relic from a catastrophic collision between galaxies? By following the evolution of galaxies in EAGLE, we may find this out.
For this experiment, we make images of all the simulated galaxies that have as many stars as the Milky Way or more at EAGLE’s ‘present day’ (14 billion years after the Big Bang) and use their galaxy family trees to take snapshots of the galaxy throughout its life, back to when the Universe was less than half its age. We make these images appear in the same way as those from the Sloan Digital Sky Survey (SDSS), which will let us compare directly to real data. Example images for three present-day galaxies can be seen in the figure above. An important aspect of the experiment is that some galaxies taken from the early EAGLE universe, which we would struggle to detect even with our most powerful telescopes, are shown as if they were local galaxies. These can take more unusual or chaotic forms. Classifying these galaxies under the same conditions as their descendents will give exciting new insight into why galaxies appear the way they do, and how they took their shape.
This is not the first time Galaxy Zoo has classified galaxies from a simulated universe: you may remember classifying images from the Illustris project, which produced valuable insight into both the models and our real Universe at the present day (see Hugh’s previous blog). We are optimistic that the different imaging techniques and inclusion of dust effects in these new images will improve the resemblance between real and simulated galaxies, and the new approach of looking at galaxies through cosmic time will lead to new discoveries.
Most of the galaxies you see on Galaxy Zoo will continue to come from our survey of the Southern sky, but EAGLE galaxies will appear no more than 20% of the time. Your classifications of these images will help scientists tremendously in understanding the evolution of galaxies. Computer experiments are the closest thing we have to a laboratory where we can test our theories of how galaxies form, and, thanks to the Galaxy Zoo, everyone can play a part!
Hi, I’m Nico. I’m a 2nd year PhD student at the University of Minnesota studying galaxies. In particular, I use statistics and machine learning to extract useful information from ever-growing galaxy catalogs astronomers have assembled over the last few decades.
Today, I get to announce a completely new project by the Galaxy Zoo team!
Galaxy Zoo: Clump Scout is a citizen science project that will take a closer look at galaxies that were classified in the Galaxy Zoo 2 project. In that project, many of you answered questions for us about their shape, structure and properties. This time we’ll be examining them in an even more detailed way.
We are searching galaxies to find “giant star-forming clumps”, or just “clumps” for short. This is what astronomers call small regions within galaxies where stars are being born at a faster-than-usual rate. They are called “giant” in comparison to any individual star or group of stars — clumps can contain millions or even billions of stars — but they’re usually quite tiny compared to the galaxy containing them. The new stars formed in clumps are brighter and more densely packed than those in the rest of the galaxy, so when photographed, clumps tend to look like small glowing areas that stand out from the background. We call any galaxy with a region like this a “clumpy galaxy”. (And yes, we promise that the word “clump” will start to sound less silly with time.)
In the Clump Scout project, we are asking volunteers to look at galaxies and click on all the clumps they can see. This is a straightforward task, but many clumps require a keen eye to pick out. Once complete, your clicks will tell us where clumps are found in thousands of galaxies in the local universe. This will be one of the first large-scale studies of clumps in local galaxies, and I’m very excited to see what we find!
Why study clumps?
Clumpy galaxies have been a bit of a mystery for scientists for a while now. Astronomers have known of their existence for decades, but discussion about them really began in the late 1990s when the Hubble telescope began to capture images of very distant galaxies. Because light takes time to travel, we saw these distant galaxies as they existed billions of years ago, at a time when the universe was still young. As we studied Hubble’s images, we started to notice differences between the early galaxies and galaxies that exist today. One such difference: In the past, nearly ALL galaxies were clumpy! Discovering this was surprising, because most galaxies in the present-day universe do not have any clumps.
It’s not yet clear how clumps were formed, why they are vanishing over time, or exactly what fraction of galaxies contain clumps. What we do know is that clumps seem to change through time alongside the galaxies that contain them. As we come to better understand clumps, we hope to better understand the role they play in the growth and evolution of their host galaxies.
Why citizen science?
Part of the reason why Clump Scout is so exciting is that this is the first time human eyes will examine so many clumpy galaxies first-hand. Thanks to the help of citizen scientists, the Clump Scout project will be able to examine over fifty thousand galaxies. To speed things along, we have already filtered these galaxies with volunteer classifications from the Galaxy Zoo 2 project and picked out the subjects that volunteers marked as having “features”. By doing this, we eliminated nearly 200,000 galaxies that are very unlikely to contain clumps, leaving only more promising subjects.
We will also be testing to see which types of clumps volunteers are able to spot. There are certain clumps that are too faint to be seen no matter where they are, while others reside in bright regions of the galaxy which drown out their signal. To quantify these effects, we have taken some galaxy images and added a few of our own, simulated clumps on top. By marking these simulated clumps, you will provide us with a wealth of information about what types of clumps we can reasonably expect to find. For example, if volunteers mark a particular simulated clump 100% of the time, it is a good sign to us that a real clump like it would be found as well. On the other hand, if no volunteers see a simulated clump, we know that similar clumps are very unlikely to be found by this project.
Why can’t computers do this?
As with many citizen science tasks, identifying clumps is fairly easy for humans to do, but difficult for computers. There have actually been a few algorithms so far that could identify clumps with some success, but it’s an exceptionally difficult task to get right. Computers must be trained to ignore all the extraneous details in an image — including background galaxies, stars in our own galaxy, and galactic features like the central bulge — to find clumps among the competing signals. Luckily, this sort of task is second nature for human beings.
Computers also tend to be very bad at finding objects they aren’t specifically instructed to find. We hope that as this project proceeds, you’ll be able to help point out some exceptionally strange clumps, or even some features we do not expect at all. It was the keen eyes of Galaxy Zoo volunteers that led to the discovery of Green Peas, a class of galaxy that is still being researched today.
This project has been in the works for the last few years, and we’re very excited to see it launch. If you’d like to try it out, you can take part here.