We are excited to announce the launch of Overlap Zoo, a new project under the Galaxy Zoo umbrella! Among the several million galaxies classified in previous Galaxy Zoo projects, a small number of those galaxies happen to overlap one another serendipitously. Now we need your help to classify these rare cosmic objects. We are launching Overlap Zoo with ~4200 candidate overlapping galaxy images from the DESI Legacy Imaging Survey, selected from previous Galaxy Zoo projects, and we invite you to help us identify which of these galaxies are truly overlapping pairs and help us categorise and catalogue them for use in future studies.

A Brief History

To understand how this project came about, it is useful to first look briefly at the work that inspired it. Prior to the start of this project, from around the 1980s through the early 2000s, astronomers began identifying and analysing a small number of individual overlapping galaxy pairs discovered by chance in various astronomical surveys. While these systems provided the astronomers at the time with valuable opportunities to study the properties of overlapping galaxies and dust, a sample consisting of only a small number of pairs was ultimately insufficient (we’ll talk more about the why later!).

This small initial sample was later expanded in 2013, when a project led by Galaxy Zoo team member and astronomer Dr. William ‘Bill’ C. Keel was conducted using imaging data from the Sloan Digital Sky Survey (SDSS) and the help of Galaxy Zoo volunteers to identify candidate overlapping pairs. Rather than relying on chance observation, this approach demonstrated that citizen science, in this case, forum science, could be used to identify a substantial number of overlapping galaxy pairs. This approach was also particularly valuable because these objects are often difficult to identify automatically, and human visual inspection is much better at distinguishing genuine overlapping pairs from apparent overlaps. 

The combined efforts of all the volunteers resulted in the creation of a new large-scale catalogue of about ~2000 overlapping pairs. While this represented a significant increase in sample size, the relatively limited image quality of SDSS made it difficult to study these systems in detail on the scales they were looking for. Nevertheless, it was still a success for classifying and categorising these systems and identifying promising candidates for follow-up observations.

Now, almost a decade later, this earlier project serves not only as the first large-scale catalogue of these objects, but also as the main inspiration for our project, Overlap Zoo. At the same time, the new generation of astronomical surveys from ground and space observatories, including Euclid, the Vera C. Rubin Observatory, the Nancy Grace Roman Space Telescope, and more, have begun, or will soon begin, providing us with higher-quality views of the Universe than ever before. With these extremely powerful new resources becoming available, we now, more than ever, have an exciting opportunity to revisit the study and cataloguing of overlapping galaxies and expand upon previous efforts through our new citizen science project on a much larger scale. 

The Science

So, what makes overlapping galaxies so special? Why do we want to study them? These are great questions that I’m sure you were asking yourselves when reading. Now, after learning about how we got here, we can finally get to the why!

When you see a beautiful picture of a galaxy, you may notice a few things right away: its colour, the different structures throughout its disk, the bright core at its centre, and all of the other features that make each galaxy unique. But what we are most interested in are those dark brown patches that you can see throughout the galaxy (like in the picture below!). These dark patches are actually dust, and they can tell us a lot about what is happening inside galaxies.

This dust, which consists of tiny particles of silicates and carbon, is an extremely important component of the galaxy, but also a pain point for many astronomers. This is because this dust absorbs and scatters (attenuates) the light of the galaxy. And because we use that light to make many important measurements, such as measuring distances, dust can cause those measurements to be inaccurate. So, astronomers must correct for this attenuation of light in their measurements. It would also be good if we had some type of universal correction that we could apply to every galaxy. And one way we hope to do this is with overlapping galaxies! 

When two galaxies happen to overlap along our line of sight, they essentially become an astrophysical laboratory for studying galactic transparency. You can think of the background galaxy as a sort of cosmic flashlight. When it lies behind a foreground galaxy, its light shines through the overlapping region, giving us a direct way to see how much light the dust in the foreground galaxy blocks. By modelling the light from both galaxies and comparing the light that passes through the foreground galaxy with the light from unobscured regions, we can create a map of the dust in the foreground galaxy and also, importantly, measure how much light each region blocks. This direct method, in comparison to other methods, is what makes these systems so valuable to us.

But not every overlapping pair is created equal. If our ultimate goal is to develop a correction that can be universally applied to every galaxy, we first need to understand dust across the many different types and configurations of overlapping pairs that exist. Some configurations can be more useful than others. There are those that are better suited for studying dust in the outer regions, while others allow us to probe into the deeper inner regions. There are also those that are more sensitive to the effects of dust and are easier to model, and vice versa. In other words, every overlapping pair that we can apply our methodology to will provide us with a slightly different piece of the whole picture of dust itself, each with its own advantages and limitations that we must consider when using these systems.

So, by creating dust maps for many different overlapping galaxies, we can begin to put those pieces together and build a more complete picture. With this information, we can hopefully provide astronomers with the data they need to better correct for the effects of dust in their measurements. But we haven’t got there yet!

The Need for a Bigger Sample

Why did we launch Overlap Zoo? What is our goal? To actually start creating those dust maps for many different pairs, we need to move beyond our relatively small sample and build a much larger one. We need as many systems as possible because, among them, we hope to find the rare, ideal configurations that are best suited for applying our technique and achieving this goal.

This is why we launched Overlap Zoo! Our goal with this project is to leverage citizen science and find as many overlapping pairs as we can to support a larger-scale study of these fascinating objects. With enough ideal systems, we could realistically have a background galaxy behind every part of a spiral galaxy—the arms, the outer disk, the inner disk, and so on. This would let us build those dust maps across different regions of spiral galaxies and see how the distribution of dust, and the amount of light it blocks, varies from galaxy to galaxy. We could do the same with elliptical galaxies, although these tend to contain less dust. With these in hand, we can see if there is a universal attenuation law that we can use for each galaxy. We are actually not entirely sure if this is the case yet, so this is extremely important for the field of astronomy as a whole! The more overlapping pairs we find, the better our chances of finding the right systems for these studies and the closer we get to finding a proper answer.

That’s where you come in. Every classification you contribute to Overlap Zoo brings us one step closer to building the sample we need to make these studies possible!

Volunteer Tasks

What will you do in our project? How can you help us? Your task in Overlap Zoo is to answer a series of questions that will help us identify the configuration of each overlapping galaxy pair. You will be presented with an image of an overlapping pair and guided through a series of questions that may feel familiar to Galaxy Zoo, but also slightly different, as we are considering not one, but two galaxies.

There will also be some questions that will be completely unfamiliar to a typical Galaxy Zoo classifier. We have tried our best to provide as much information as possible so that you can confidently answer these questions. We encourage you to take your time and refer to the field guides or other resources whenever you are unsure about how to classify a particular pair.

To get involved in our project, head over to Overlap Zoo and start classifying now! We look forward to your classifications and your help in expanding our catalogue of these objects for future research!

We look forward to your help,

Trevor Butrum and Benne Holwerda.

Hi everyone, 

If you’ve been following Galaxy Zoo for a while, you may remember the Galaxy Zoo: Clump Scout project. Now, Clump Scout is back, and we need your help with Galaxy Zoo: Clump Scout II!

The first Clump Scout project
Back in 2019, we asked you to help us find giant star forming clumps (or just “clumps”) in nearby galaxies. With your help, the first Clump Scout project was a resounding success. Over 14,000 volunteers took part, looking at nearly 60,000 galaxy images from the Sloan Digital Sky Survey (SDSS) and making millions of individual classifications. When the project completed, you had helped us identify clumps in more than 7,000 galaxies, giving us what was then the largest catalogue of clumpy systems in the local Universe. 

What have we been doing since Clump Scout finished?
We’ve learned a lot from the results of Galaxy Zoo: Clump Scout and they provided a crucial foundation for the research that followed. By using the results to train a Deep Learning model, we were able to discover 41,445 bright clumps in 34,246 galaxies by searching over 240,000 SDSS galaxy images. This expanded clumpy galaxy sample allowed us to investigate whether clumpy galaxies are more prevalent in dense galaxy clusters or in the sparse voids between clusters.  

Since then, we have continued to update our clump detection deep learning model and we have tried to fine-tune it to detect clumps in survey data from more powerful telescopes like the Subaru Telescope, and most recently the Euclid Space Telescope. These telescopes allow us to find fainter, smaller clumps in more distant galaxies and they produce sharper images that start to reveal the individual clumps’ substructure. 

New images

The three images in the figure below show the same galaxy as seen by the SDSS, the Hyper Suprime-Cam (HSC) camera on the 8-metre-diametre Subaru Telescope, and the two cameras on board the Euclid Space Telescope. The galaxy is barely visible in the SDSS image and certainly doesn’t show any obvious signs of being clumpy. In the HSC image the galaxy is clearly visible and shows several clumps that appear as bright, somewhat blurry blobs lying along the galaxy’s spiral arms. The Euclid image is much sharper and shows that the individual clumps from the HSC have complex substructure.

Figure 1. Three images of the same clumpy galaxy as seen by the SDSS Survey’s Apache Point 4 metre class telescope (left), the Hyper Suprime-Cam imager on the 8-metre diameter Subaru Telescope (centre) and the VIS and NIR cameras on the Euclid Space Telescope (right).

New science

Clumps are sites of intense star formation, which can deliver energy to their surroundings via several processes, which are often described collectively as “feedback”. For example, stellar radiation, stellar winds (streams of fast-moving charged particles launched from stars’ surfaces) and supernova explosions can all inject energy into the interstellar medium in and around the clumps. These feedback processes can have profound implications for the effect of clumps on galaxies’ growth and evolution. However, the effects of feedback depend crucially on how well it transfers energy out of the clump and how long the clump survives before being disrupted by material in their host galaxy’s disk. Both factors remain poorly constrained by observations.

How can high resolution Euclid images help to reveal the impact of feedback from clumps? Well, the Euclid images give us the ability to observe and study the detailed substructure of clumps. This is very useful because it allows us to compare real clumps with those produced in high resolution simulations of clumpy galaxies. An example comparison is shown in the figure below. These high-resolution simulations suggest that clump substructural morphology (the distribution of sub-clump shapes and sizes) is strongly correlated with feedback-related properties of clumps, including their longevity and how much energy from their internal star formation they can impart to their surroundings. 

Figure 2. A clumpy galaxy observed with Euclid (left) compared to a simulated clumpy galaxy (right). With the detailed images we are now getting from Euclid, we can begin to study the substructures of clumps that we see should be there in simulations, telling us about clump properties like how long they can survive.

If clumps are resilient to disruption, then tidal forces are expected to make them migrate slowly towards the centres of their host galaxies. Feedback that is generated by the clumps’ as they migrate can regulate star formation in their host galaxies and may even drive gas and dust into the surrounding interstellar medium. Once clumps reach their host galaxy’s centres, they can dissolve and contribute to the growth of galaxy bulges.

The physical properties of simulated clumps can be directly extracted from the simulation data. If we find populations of real and simulated clumps with matching substructural morphology, then we will be able to infer that the physical properties of the real clumps are similar to the known properties of the simulated clumps.

New challenges

The new Euclid images provide new insights into the physics of clumps and clumpy galaxies but they also bring some challenges. Our clump detection model was trained using images with relatively coarse spatial resolution in which clumps normally look like blurry blobs. The complex substructure of the clumps in the Euclid images makes them more difficult for our model to identify. Our model that worked so well on low resolution images now gets confused and starts to mistake other objects, like foreground stars and background galaxies for clumps. Teaching the model to avoid these mistakes is one of the main purposes of Galaxy Zoo: Clump Scout II.

New project

Your task in Galaxy Zoo: Clump Scout II is to correct the clump labels generated by our model. You will be shown an image of a galaxy that our model thinks is clumpy with different coloured boxes, representing our model’s labels, overlaid. The figure below shows an example of the classification interface. 

Figure 3. The Galaxy Zoo: Clump Scout II interface

The different box colours represent different types of astrophysical object – green for clumps, yellow for foreground stars, blue background galaxies and red for galaxies’ central bulges. We need you to examine all the boxes, make sure they surround real objects and check that those objects are marked with boxes of the correct colour. We also need you to mark any clumps that our model has missed. Your corrected labels will be used to incrementally retrain our model until it is able to accurately find all the clumps and contaminating clump-like objects in Euclid images. 

Our finetuned model will ultimately allow us to search over 250 million Euclid galaxy images for clumps and assemble an enormous catalogue of clumps to analyse, helping to reveal the internal physical processes that drive the evolution of clumps, their host galaxies and their extragalactic surroundings.To get involved head over to Galaxy Zoo: Clump Scout II and start classifying today! 

This project makes use of Q1 data from European Space Agency’s Euclid mission; learn more here: cosmos.esa.int/web/euclid/euclid-q1-data-release