Galaxy Zoo: Clump Scout II

Clumpy Galaxies

In the nearby Universe, most galaxies can be categorized as spirals, ellipticals, or irregular systems. However, in the more distant (and younger) Universe, galaxy shapes were more diverse and galaxies with "clumpy" structures dominate. The image below shows eight images of these distant clumpy galaxies that were taken by the Hubble Space Telescope (HST) and classified during the Galaxy Zoo: Hubble (GZH) project.

Galaxies of the past went through a period of intense star formation which is thought to be largely responsible for these unusual shapes. This period of star formation has since died off and most galaxies today have settled down into the types we see in the local Universe.

Galaxy Zoo: Clump Scout

The results of the first Galaxy Zoo: Clump Scout project were based on your Zooniverse volunteers' annotations of images from the Sloan Digital Sky Survey (SDSS) Legacy Survey. They showed us that the drop in star formation throughout the Universe has made nearby clumpy galaxies are very rare. Less than 5% of star forming galaxies in the nearby Universe exhibit obvious clumps. The next image shows eight SDSS images of those rare, nearby clumpy galaxies.

These nearby galaxies look remarkably similar to the galaxies seen by Hubble in the distant Universe. That's because, coincidentally, the physical sizes of structures that can be resolved by HST in distant galaxies are roughly the same as those that can be resolved by SDSS in nearby galaxies. To put it another way, distant galaxies appear equally sharp (or blurry) in HST images as nearby galaxies do in SDSS images.

Galaxy Zoo: Clump Scout II

Now, Galaxy Zoo: Clump Scout is back, with higher resolution images and new questions to answer! In this project you will be inspecting images taken by the Visible Imager Instrument (VIS) and Near-infrared Spectrometer and Photometer (NISP) instruments mounted on the [Euclid space telescope] (https://euclid.caltech.edu/).

Euclid images are much sharper and more detailed than SDSS images which makes identifying clumps in galaxies much easier and makes it possible to see galaxies that are further away in more detail. This is because Euclid operates outside of the Earth’s atmosphere whereas SDSS is ground based, which means it is not affected by weather and its images are not distorted by light passing through the Earth’s atmosphere. Additionally, the VIS camera uses 36 high-resolution CCDs with about 600 megapixels in total whereas SDSS’s imaging camera only has about 120 megapixels across its 30 CCDs.
What appear like bright symmetrical blobs in the SDSS images are now resolved into more complicated shapes that tell us about the internal substructure of the clumps themselves. Measuring this substructure and comparing it with the predictions of high-resolution galaxy simulations can give us clues about how the clumps formed and how they affect their host galaxies and the intergalactic space around them. The next image shows a comparison of how SDSS (left), HSC (centre) and Euclid (right) see the same clumpy galaxy. The galaxy is barely visible in SDSS images, but HSC sees it clearly and some of the clumps have distinctly asymmetric shapes. In the Euclid image, it is clear that what seem to be single bright blobs in SDSS and HSC images are actually groups of smaller star-forming regions with complicated substructure The classifications you provide in this project will help us to fine-tune our model to analyse the images from the next data releases from Euclid when they arrive.

Accelerating Clump Detection with Deep Learning

The Euclid space telescope was designed as a survey instrument. Eventually, when its survey is complete the Euclid telescope will have taken images of over 1 billion galaxies and about 250 million of those will be well enough resolved to detect any clumps they might contain. The only realistic way to find the clumps in such large number of galaxies is using machine learning, and one of the main goals of this project is to help build the machine learning models we need. We have already made some progress. Using the results from the first Galaxy Zoo: Clump Scout project, we trained a deep learning (sometimes called AI) model that can detect and label clumps within galaxies in SDSS images. Then we trained another model useing a small number of galaxies that had been seen by SDSS and Euclidto train a similar model that draws outlines around clumps in Euclid images. However, the Euclid model isn't perfect. Sometimes it identifies stars in our own galaxy as clumps in a distant galaxy, sometimes it misses real clumps, and sometimes the regions it identifies as clumps aren't accurate enough. We need your help to correct and refine the model's predictions.

Unanswered questions

We still don't understand the details of how clumps form. Is it due to mergers between galaxies, or due to instabilities within the galaxies themselves that cause large clouds of gas to collapse and rapidly form stars? Is it a mixture of both mechanisms? Simulations of galaxy formation and evolution suggest that different clump formation pathways lead to clumps with different substructural properties. By measuring the shapes of clumps in this project you can help us to unravel the mysteries of clump formation. We also don't fully understand what happens to the clumps over time. Some models predict that these large clumps eventually migrate to the center of the galaxy to form the galactic bulge. Other models suggest that these clumps are short lived and dissipate before they can migrate to the center. Determining which scenario is correct could have dramatic implications about the formation and evolution of disk galaxies. In this project you will help us identify clumps in many more galaxies than the original Galaxy Zoo: Clump Scout. You will be detecting fainter and more distant clumpy galaxies that existed earlier in the Universe's history. Finally you will be helping to prepare for the fantastic images that will be delivered from Euclid over the coming decade. These data will help to answer the questions we have now, but we'd be amazed if they didn't introduce many new ones.

As always, we couldn't do it without you!