Archive by Author | hughfromthezoo

Introducing Galaxy Zoo: Clump Scout, a new citizen science project

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.)

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Figure 1: Some examples of clumpy galaxies that will appear in Galaxy Zoo: Clump Scout. In these images, clumps look like small, blue spots on the galaxies. Some of the clumps in these images are bright and obvious, while others take a bit more care to spot. All photos were taken by the Sloan Digital Sky Survey.

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!

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Figure 2: A classification from Galaxy Zoo: Clump Scout. Here, a red icon marks the central bulge of the galaxy, while six green icons mark clumps.

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.

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Figure 3: An example galaxy before and after simulated clumps were added to it. On the right, a total of 5 extra clumps have been added, but several are too faint to be seen in this image.

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.

Spectracular Performance!

During the past 10 years Galaxy Zoo volunteers have done amazing work helping to classify the visual appearance (or “morphology”) of distant galaxies, which has enabled fantastic science that wouldn’t have been possible without your help. 

Morphology alone encodes a wealth information about the physical processes that drive the formation and ongoing evolution of galaxies, but we can learn even more if we analyze the spectrum of light they emit.

For the 100th Zooniverse project we designed the Galaxy Nurseries project to get your help analyzing galaxy spectra obtained by the Hubble Space Telescope (you can find many more details about Galaxy Nurseries on the main project research pages and this previous blog post).

If you participated in Galaxy Nurseries, then the data you analyzed were generated using a technique called slitless spectroscopy. In slitless spectroscopy all the light entering the HST aperture is dispersed (or split) into its separate frequencies before being projected directly into the telescope’s camera. Figure 1 illustrates a typically confusing result!

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Figure 1: Example of data obtained by the Hubble Space Telescope using slitless spectroscopy.

Each bright horizontal streak in the image shown in Figure 1 is actually the spectrum of a different galaxy or star. Analyzing these data can be very tricky, especially when nearby galaxy spectra overlap and cross-contaminate each other. Automatic algorithms really struggle to reliably distinguish between spectral contamination and scientifically interesting features that are present in the spectra. This means that scientists almost aways need to visually inspect any features that are automatically detected in order to ensure that they are really there!

In Galaxy Nurseries, we asked volunteers to help with this verification process. We asked you to double-check over 27,000 automatically detected emission lines in galaxy spectra obtained by the WISP galaxy survey, labelling them as either real or fake. Even for professional astronomers and experienced Galaxy Zoo volunteers, verifying the presence of emission lines in slitless spectroscopic data can be very difficult. To help you discriminate between real and fake emission lines we showed you three different views of the data. Figure 2 shows an example of one of the Galaxy Nurseries subject images.

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Figure 2: A Galaxy Nurseries subject showing a real emission line. The different panels show A) a 1-dimensional representation of the spectrum with the potential emission line marked ; B) a 2-dimensional “cutout” from the full slitless spectroscopic image, with the potential emission line and the expected extent of the galaxy spectrum marked; C) a direct image of the galaxy for which the spectrum was generated.

As well as the 1 dimensional spectrum shown in Figure 2 (Panel A), we also showed a “cutout” from the full slitless spectroscopic image, which isolated the target spectrum (Panel B), and a direct image of the galaxy that produced the spectrum (Panel C). The cutout in Panel B can be really useful for identifying contamination from adjacent spectra. For example, something that looks like a feature in the target spectrum might actually originate in an adjacent spectrum and would therefore appear slightly vertically off-centre in the 2-dimensional image.

Why is the direct image useful for spectroscopic analysis? Well, emission lines often appear like very slightly blurred images of the target galaxy at a specific position in the slitless spectrum. Look again at the emission line and the direct image in Figure 2. Can you see the similarity? If the shape of the automatically detected line feature in the slitless spectroscopic image doesn’t match the shape of the galaxy in the direct image, then this can indicate that the feature is just contamination masquerading as an emission line.

The response to Galaxy Nurseries was fantastic! Following its launch the project was completed in only 40 days, gathering 414,360 classifications (that’s 15 classifications per emission line) from 3003 volunteers. Huge thanks for everyones’ help! The results of the project were published in a Research Note, and the rest of this post summarizes what we learned.

Using the labels assigned to each potential emission line by galaxy zoo volunteers we computed the fraction of volunteers who classified the line and thought it was real (hereafter freal). We wanted to compare the responses of the Galaxy Zoo volunteers with those of professional astronomers from the WISP survey team (WST). To do this, we divided the potential emission lines into two sets. The verified set contained emission lines that the WST thought were real and the vetoed set contained emission lines that the WST thought were fake. We assumed that the WST assessments were correct in the vast majority of cases, but this might not be completely accurate. Even professional astronomers make mistakes!

Figure 3 shows the distributions of freal for the two sets of emission lines. The great news is that for the vast majority of lines that the WST thought were fake, over half of the volunteers agreed with them (i.e. freal < 0.5). Similarly for most of the WST-verified set of line, the majority volunteers also labeled them as real. These results show us that Zooniverse and Galaxy Zoo volunteers are very capable when it comes to separating real emission lines from the fakes.

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Figure 3: The distributions of freal for sets of emission lines that were verified (blue) or vetoed (orange) by the WISP survey team.

What can we say about the lines for which the volunteers and the WST disagreed? Is there something about them that makes them particularly hard to classify? Well, it turns out that the answer is “yes”!

We computed two statistical metrics to quantify the level of agreement between the Zooniverse volunteers and the WST for a particular sample of the emission lines that were classified.

  1. The sample purity is defined as the ratio between the number of true positives (for which both the volunteers and the WST believe the the line is real)  and the combined number of true positives and false positives (for which a feature labeled as fake by the WST was labeled as real by the volunteers). The purity tells us the fraction of lines in the sample that were labeled real by the volunteers that were also labeled as real by the WST. If volunteers don’t mislabel any fake lines as real then purity is 1.
  2. The sample completeness is the ratio between the number of true positives and combined number of true positives and true negatives (for which the WST labeled the line as real, but the volunteer consensus was that the line was fake). The completeness tells us the fraction of lines in the sample that were labeled as real by the WST that were also labeled as real by the volunteers. If volunteers spot all the real lines identified by the WST then the completeness is 1.

Figure 4 plots purity and completeness as a function of freal  and the emission line signal-to-noise ratio (S/N). Lines with higher S/N stand out more relative to the noise in the spectrum and should be easier to analyze for volunteers and the WST alike. Examining Figure 4 reveals that for subsets of candidate lines having freal less than a particular threshold value (shown on the horizontal axis), the completeness values are higher for higher S/N. This indicates that spotting real lines is much easier when the features being examined are bright, which makes intuitive sense. On the other hand, higher purities can be achieved for similar threshold values of  freal as the S/N value decreases, which indicates that volunteers are reluctant to label faint lines as real. At low S/N, sample purities as high as 0.8 can be achieved when only 50% of volunteers agreed that the corresponding emission lines were real. At higher S/N, volunteers become more confident, but also seem slightly more likely to identify noise and contaminants as real lines. This is probably a reflection of just how difficult the line identification task really is. Nonetheless, samples that are 70% pure can be selected by requiring a marginal majority of votes for real ( freal value of at least 0.6), which is pretty impressive!

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Figure 4: Sample purity (left) and completeness (right) plotted as a function of minimum freal value for any potential line in the sample, and that line’s signal-to-noise ratio.

We can use the plots in Figure 4  to select samples that have desirable properties for scientific analysis. For example, if we want to be sure that we include 75% of all the real lines but we don’t mind a few fakes sneaking in, then we could choose  freal = 0.5 which would give a completeness larger than 0.75 for all S/N values. However, if we choose freal = 0.5, then the purity of our sample could be as low as 0.6 for high S/N, with about 40% of accepted lines being fake in reality.

The ability to extract very complete but impure emission line samples can be very useful. By selecting a sample that removes a sizable fraction of fakes from the automatically detected candidates, the number of potential lines that the WST need to visually inspect is dramatically reduced. It took the WST almost 5 months before each line in Galaxy Nurseries could be inspected by just two independent astronomers. By providing 15 independent classifications for each line, Zooniverse volunteers did the 8 times as much work in just 40 days! In the future, large-scale slitless spectroscopic surveys will be performed by new space telescopes like Euclid and WFIRST. These surveys will measure millions of spectra containing many millions of potential emission lines and individual science teams will simply not be able to visually inspect all of these lines. Eventually, deep learning algorithms may be able to succeed where current automatic algorithms fail. In the meantime, it is only with the help of Zooniverse and Galaxy Zoo volunteers that scientists will be able to exploit more than the tiniest fraction of the fantastic data that will soon arrive.

Classifying Galaxies from Another Universe!

We’re excited to announce the publication of another scientific study. that wouldn’t have been possible without the hard work of the Galaxy Zoo volunteers. The paper:

“Galaxy Zoo: Morphological classification of galaxy images from the Illustris simulation”

is the first Galaxy Zoo publication that examines visual morphological classifications of computer-generated galaxy images. The images were produced in collaboration with the international team of scientists who  implemented and analyzed the highly sophisticated Illustris cosmological simulation (you can find many more details about Illustris on the main Illustris project website and about the Galaxy Zoo: Illustris project in this previous blog post). Illustris is designed to accurately model the evolution of our Universe from a time shortly after its birth until the present day. In the process, simulated particles of dark matter, gas, and stars aggregate and condense to form galaxy clusters that contain seemingly realistic galaxies. In our paper we wanted to test the realism of those simulated galaxies by inviting Galaxy Zoo volunteers to evaluate their morphological appearance. We wanted to know whether Illustris galaxies look like real galaxies.

But where to start looking? Well, if you’ve ever classified a galaxy on Galaxy Zoo then you must have answered a question worded something like:

Is the galaxy simply smooth and rounded, or does it have features?

This question represents one of the simplest ways to distinguish between different groups of galaxies, but its answer can reveal a lot of information about a galaxy’s history, as well as its current activity. Visible features and substructure like discs, spiral arms and bars in galaxy images often indicate sites of ongoing star formation and can provide evidence for complex dynamical processes within a galaxy. On the other hand, apparently featureless galaxies may have formed in dense environments where galaxy-galaxy interactions are more common and might act to destroy features or even prevent them from forming in the first place.

In our paper, we compared the prevalence of visible features in galaxy images that were produced using Illustris against an equivalent sample of real galaxy images that were derived from Sloan Digital Sky Survey (SDSS) observations. Some of the differences we found were surprising but quite illuminating!

Each image in Galaxy Zoo is classified by about forty volunteers and their votes for each question are aggregated to obtain a consensus. The level of agreement between volunteers can be quantified using the vote fraction for a particular response. For a particular image and question the vote fraction for a possible response is just the number of volunteers who voted for that response, divided by the total number of votes cast for that question, for that image. A concrete example that applies here is the “featured” vote fraction: the number of volunteers who classified a galaxy image as exhibiting visible features divided by the total number of votes cast for the simple question that was quoted above. Vote fractions close to zero indicate that most volunteers thought the galaxy was smooth and rounded, while vote fractions around one imply almost unanimous consensus that a galaxy has visible features.

The filled green bars in Figure 1 illustrate the distribution of this “featured” vote fraction for real galaxy images. The distribution is dominated by a peak close to zero, which means that most volunteers thought that most galaxies looked smooth and featureless. There is also a smaller peak close to one, corresponding to a population of obviously featured galaxies. In contrast, the blue line shows the “featured” vote fraction for Illustris galaxy images. The bulk of the distribution is now peaked around 0.6, which means that Illustris galaxies were generally perceived to be predominantly featured. However, there are very few Illustris galaxies that were unanimously labeled as exhibiting visible features and a substantial population of visibly smooth galaxies is also present. Overall, the Illustris galaxy images seem more feature rich, but perhaps slightly more ambiguous than their SDSS counterparts.

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Figure 1: The distributions of the “featured” vote fraction for real (SDSS; Green, filled bars) and simulated (Illustris; Blue, hollow bars) galaxy images. There is an obvious mismatch between the distributions for the simulated and real galaxy images.

To try to understand the origin of the mismatch between Illustris galaxies and those in the real Universe, we separated both of the image samples into three sub-groups based on the total mass of the stars that the galaxies contain (more succinctly described as their “stellar mass”).  Each of the panels in Figure 2 can be interpreted in the same way as Figure 1, except that they correspond only to the galaxies for each of the three stellar mass sub-groups. The two panels to the left are for galaxies with stellar masses less than the mass of 1000 billion suns. They look remarkably similar to Figure 1 with the SDSS and Illustris distributions matching very poorly. However, the situation changes markedly in the right-hand panel. For these extremely massive galaxies, it appears that the Illustris simulation reproduces the observed proportion of visibly featured galaxies much better, although the population of unambiguously featured galaxies is still absent.

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Figure 2: The distributions of the “featured” vote fraction for real (SDSS; Green, filled bars) and simulated (Illustris; Blue, hollow bars) galaxy images. The distributions are shown for sub-groups of galaxies that are subdivided according to stellar mass. The distributions for the most massive galaxies, with stellar masses greater than the mass of 1000 billion suns, are shown in the right-hand panel. It is only for these very massive galaxies that the vote fraction distributions for simulated and real galaxies begin to look similar.

The change in behavior with stellar-mass that we have identified might simply be an artifact of the finite resolution at which Illustris is able to simulate the Universe. Computational power is limited, so Illustris cannot accurately model the positions, interactions and evolution of every star in its simulation volume (and of course tracking individual gas atoms or dark matter particles is completely impossible!). Instead, Illustris models large groups of stars, and large accumulations of gas and dark matter as single “particles” and models the way that they interact with each other. The features that volunteers perceive in Illustris galaxy images manifest substructures formed by groups of many such particles. Simulated galaxies with larger stellar masses contain more stellar particles that enable the simulation to model finer structural details which may be necessary to emulate the appearance of real galaxies.

Studies involving automatic morphological classification of Illustris galaxy images (e.g. Bottrell et al 2017, Snyder et al 2015) have also identified a marked divergence with galaxies in the real Universe below the same 1000 billion solar mass limit that we have found. Confirmation that the visual appearance of galaxies also changes perceptibly complements a growing body of knowledge on this subject.

Dust is another constituent of galaxies that can substantially modify their appearance by absorbing bluer light that typically indicates star formation and re-emitting it at redder wavelengths. This dust reddening effect is not accounted for by the Illustris simulations and could obscure the visibility of features that are actually present in real galaxies. This means that Illustris might be modeling real galaxies better than it seems, and coupling of a dust reddening model to the simulation output might improve the correspondence between the mismatched vote fraction distributions at lower stellar masses.

As is often the case in scientific research, an unanticipated result has provided valuable insight. The results from Galaxy Zoo: Illustris will help cosmologists to improve their models as they develop the next generation of large-scale simulations of our Universe. The results also underline the ongoing potential utility for visual morphological classification of simulated galaxies. The most recent cosmological simulations, including a next-generation Illustris Simulation,  address many of the shortcomings that this and other studies have revealed. Comparing their outputs with SDSS galaxy images, as well as observational data produced by other surveys, will undoubtedly yield more insights into the processes that govern the formation and evolution of galaxies. Watch this space!

A preprint of the new paper, which has been accepted by the Astrophysical Journal, can be downloaded from the arXiv.