Excited to join in? Click here to go to Galaxy Zoo and start classifying! What could you discover?
We’ve just switched on what may be the biggest change to Galaxy Zoo since the project started more than a decade ago. In order to prepare for future surveys like Euclid and LSST which might overwhelm even the stalwart efforts of Galaxy Zoo volunteers, we’re now running an automatic classifier which works with those results from volunteers.
This machine – even when trained on the existing Galaxy Zoo results – is not perfect, and so we still need classifications from you all. Each night, the machine will learn from the day’s results, and then calculate which galaxies it thinks it most needs human help with – and if you select the ‘Enhanced’ workflow, then you’ll be much more likely to see these galaxies.
You can read more about the machine learning we’re using in a blogpost from Mike Walmsley here, and in more technical detail here. (There’s a paper available on the arXiv from this morning too). We’re also running a messaging experiment you can read about here.
We do still need volunteers to look at each and every galaxy to make sure we’re not missing anything. If you prefer to classify the old-fashioned way, then the ‘Classic’ workflow is Galaxy Zoo just as it always was.
I and the rest of the team are looking forward to seeing what we can find with this new approach – and with your help.
Alongside the new workflow that Galaxy Zoo has just launched (read more in this blog post: https://wp.me/p2mbJY-2tJ), we’re taking the opportunity to work once again with researchers from Ben Gurion University and Microsoft Research to run an experiment which looks at how we can communicate with volunteers. As part of this experiment volunteers classifying galaxies on the new workflow may see short messages about the new machine learning elements. Anyone seeing these messages will be given the option to withdraw from the experiment’; just select the ‘opt out’ button to avoid seeing any further messages.
After the experiment is finished we will publish a debrief blog here describing more of the details and presenting our results.
This messaging experiment has ethics approval from Ben Gurion University (reference: SISE-2019-01) and the University of Oxford (reference: R63818/RE001).
Since I joined the team in 2018, citizen scientists like you have given us over 2 million classifications for 50,000 galaxies. We rely on these classifications for our research: from spiral arm winding, to merging galaxies, to star formation – and that’s just in the last month!
We want to get as much science as possible out of every single click. Your time is valuable and we have an almost unlimited pile of galaxies to classify. To do this, we’ve spent the past year designing a system to prioritise which galaxies you see on the site – which you can choose to access via the ‘Enhanced’ workflow.
This workflow depends on a new automated galaxy classifier using machine learning – an AI, if you like. Our AI is good at classifying boring, easy galaxies very fast. You are a much better classifier, able to make sense of the most difficult galaxies and even make new discoveries like Voorwerpen, but unfortunately need to eat and sleep and so on. Our idea is to have you and the AI work together.
The AI can guess which challenging galaxies, if classified by you, would best help it to learn. Each morning, we upload around 100 of these extra-helpful galaxies. The next day, we collect the classifications and use them to teach our AI. Thanks to your classifications, our AI should improve over time. We also upload thousands of random galaxies and show each to 3 humans, to check our AI is working and to keep an eye out for anything exciting.
With this approach, we combine human skill with AI speed to classify far more galaxies and do better science. For each new survey:
- 40 humans classify the most challenging and helpful galaxies
- Each galaxy is seen by 3 humans
- The AI learns to predict well on all the simple galaxies not yet classified
What does this mean in practice? Those choosing the ‘Enhanced’ workflow will see somewhat fewer simple galaxies (like the ones on the left), and somewhat more galaxies which are diverse, interesting and unusual (like the ones on the right). You will still see both interesting and simple galaxies, and still see every galaxy if you make enough classifications.
With our new system, you’ll see somewhat more galaxies like the ones on the right, and somewhat fewer like the ones on the left.
We would love for you to join in with our upgrade, because it helps us do more science. But if you like Galaxy Zoo just the way it is, no problem – we’ve made a copy (the ‘Classic’ workflow) that still shows random galaxies, just as we always have. If you’d like to know more, check out this post for more detail or read our paper. Separately, we’re also experimenting with sending short messages – check out this post to learn more.
Myself and the Galaxy Zoo team are really excited to see what you’ll discover. Let’s get started.
This is a technical overview of our recent paper (Walmsley 2019) aimed at astronomers. If you’d like a more approachable introduction to how machine learning affects Galaxy Zoo, check out this blog [link TODO]
I’d love to be able to take every galaxy and say something about it’s morphology. The more galaxies we label, the more specific questions we can answer. When you want to know what fraction of low-mass barred spiral galaxies host AGN, suddenly it really matters that you have a lot of labelled galaxies to divide up.
But there’s a problem: humans don’t scale. Surveys keep getting bigger, but we will always have the same number of volunteers (applying order-of-magnitude astronomer math).
We’re struggling to keep pace now. When EUCLID (2022), LSST (2023) and WFIRST (2025ish) come online, we’ll start to look silly.
To keep up, Galaxy Zoo needs an automatic classifier. Other researchers have used responses that we’ve already collected from volunteers to train classifiers. The best performing of these are convolutional neural networks (CNNs) – a type of deep learning model tailored for image recognition. But CNNs have a drawback. They don’t easily handle uncertainty.
When learning, they implicitly assume that all labels are equally confident – which is definitely not the case for Galaxy Zoo (more in the section below). And when making (regression) predictions, they only give a ‘best guess’ answer with no error bars.
In our paper, we use Bayesian CNNs for morphology classification. Our Bayesian CNNs provide two key improvements:
- They account for varying uncertainty when learning from volunteer responses
- They predict full posteriors over the morphology of each galaxy
Using our Bayesian CNN, we can learn from noisy labels and make reliable predictions (with error bars) for hundreds of millions of galaxies.
How Bayesian Convolutional Neural Networks Work
There’s two key steps to creating Bayesian CNNs.
1. Predict the parameters of a probability distribution, not the label itself
Training neural networks is much like any other fitting problem: you tweak the model to match the observations. If all the labels are equally uncertain, you can just minimise the difference between your predictions and the observed values. But for Galaxy Zoo, many labels are more confident than others. If I observe that, for some galaxy, 30% of volunteers say “barred”, my confidence in that 30% massively depends on how many people replied – was it 4 or 40?
Instead, we predict the probability that a typical volunteer will say “Bar”, and minimise how surprised we should be given the total number of volunteers who replied. This way, our model understands that errors on galaxies where many volunteers replied are worse than errors on galaxies where few volunteers replied – letting it learn from every galaxy.
2. Use Dropout to Pretend to Train Many Networks
Our model now makes probabilistic predictions. But what if we had trained a different model? It would make slightly different probabilistic predictions. We need to marginalise over the possible models we might have trained. To do this, we use dropout. Dropout turns off many random neurons in our model, permuting our network into a new one each time we make predictions.
Below, you can see our Bayesian CNN in action. Each row is a galaxy (shown to the left). In the central column, our CNN makes a single probabilistic prediction (the probability that a typical volunteer would say “Bar”). We can interpret that as a posterior for the probability that k of N volunteers would say “Bar” – shown in black. On the right, we marginalise over many CNN using dropout. Each CNN posterior (grey) is different, but we can marginalise over them to get the posterior over many CNN (green) – our Bayesian prediction.
Read more about it in the paper.
But right now, we’re still showing hundreds of thousands of these to humans. That’s not efficient. And it’s not fair on our volunteers.
Ideally we would only show the volunteers the images that the model most needs help with. The model should be able to ask – hey, I’m really uncertain about these galaxies – can you label them for me please? Then the humans would label them, the model would retrain with only those most informative galaxies. This is active learning.
In our experiments, applying active learning reduces the number of galaxies needed to reach a given performance level by up to 35-60% (See the paper, Figures ).
Previous Galaxy Zoo CNNs didn’t account for the uncertainty in training labels. This is a problem because when you’re trying to train your model, you don’t know if the errors your model makes are because the model is wrong, or just because the label is really uncertain.
Previous CNNs were also not designed to make probabilistic predictions. When they predict that, say, 30% of volunteers would call a galaxy “barred”, there’s no limiting the reliability of conclusions drawn using such methods.
This is only possible because we think about labels probabilistically (p(v|w)) and approximate training many models (p(w|d)).
The Galaxy Zoo science team has to choose which surveys to classify and, for those surveys, reduce the number of independent classifications per galaxy. The speed at which we can accurately classify galaxies severely limits the scale, detail, and quality of our catalogues. This makes it harder for us, and the researchers who rely on us, to do good science.
Congratulations Radio Galaxy Zoo citizen scientists on a job well done! The Radio Galaxy Zoo 1 project has now finished with ~2.29 million classifications! Well done on helping us push towards the finish line.
We have at least two second-generation Radio Galaxy Zoo projects in the pipeline for which we hope to launch next. Therefore please stay tuned for the announcement of the Radio Galaxy Zoo 2 projects where we will be presenting you with new data from the next-generation radio telescopes.
Thank you very much again for all your support and we will continue to keep you updated on our progress in the interim.
Ivy & Stas
I’m delighted to announce the acceptance of another paper based on your classifications at Galaxy Zoo, “Galaxy Zoo: Unwinding the Winding Problem – Observations of Spiral Bulge Prominence and Arm Pitch Angles Suggest Local Spiral Galaxies are Winding”, which has just been released on the arxiv pre-print server, and appear in the Monthly Notices of the Royal Astronomical Society (MNRAS) soon.
Here’s the title and author page.
This paper has been a long time coming, and is based significantly on the excellent thesis work of Ross Hart (PhD from Nottingham University). Ross wrote about some of his work for the blog previously “How Do Spiral Arms Affect Star Formation“. One of the things Ross’s PhD work showed was just how good your identification of spiral arm winding is, and that allowed us to be confident to use it in this paper.
You might notice the appearance of some of your fellow citizen scientists in this author list. Dennis, Jean and Satoshi provided help via the “Galaxy Zoo Literature Search” call which ended up contributing significantly to the paper.
Our main result is that we do not find any significant correlation between how large the bulges are and how tightly wound the spirals are in Galaxy Zoo spiral galaxies…. this non-detection was a big surprise, because this correlation is discussed in basically all astronomy text books – it forms the basis of the spiral sequence described by Hubble.
Way back in 1927 Hubble wrote (about the spiral nebula he had observed) that: “three [properties] determine positions in the sequence: (1) the relative size of the unresolved nuclear region, (2) the extent to which the arms are unwound (the openness or angle of the spiral), (3) the degree of condensation in the arms.” He goes on to explain that “These three criteria are quite independent, but as an empirical fact of observation they develop in the same direction, and can be treated as various aspects of the same process.” (i.e. Hubble observed them to be correlated).
It’s been known for a long time that there are examples where bulge (or “unresolved nuclear region”) size and arm winding did not agree, but these are usually treated as exceptions. What we’ve shown in this paper, is that for a sample selection which goes beyond just the brightest nearby galaxies Hubble could see, the correlation is not strong at all. Below is an annotated version of our main result figure – each point is a spiral with Galaxy Zoo classifications, and the contours show where there are lots of points. We find spirals all over this plot (except not many with big bulges and loosely wound arms), and the red and blue lines show the lack of any strong trend in either direction.
This has significantly implications for how we interpret spiral winding angles, and could be explained by many/most spiral arms winding up over time (at rates which depend on the bulge size) rather than being density waves. We need to do more work to really understand what this observation tells us (which is a great place to be in science!).
We have also known for a while, that bulge size correlates best with modern expert galaxy classification on the Hubble sequence (e.g. when we compared you classifications to the largest samples done in that way). So another point we make in this paper is how different these modern classifications are to the traditional classifications done by Hubble and others. That’s OK – classifications should (and do) shift in science (part of the scientific method is to change on the basis of evidence), but it does mean care needs to be taken to be precise about what is meant by “morphology of galaxies”.
I ended the abstract of the paper with: “It is remarkable that after over 170 years of observations of spiral arms in galaxies our understanding of them remains incomplete.” and I really think that’s a good place to end. Galaxy morphology provides a rich source of data for understanding the physics of galaxies, and thanks to you we have access to the largest and most reliable set of galaxy morphologies ever.
Here is a bittersweet announcement that the current first-generation Radio Galaxy Zoo project will be retiring on the 1st May 2019. We are so grateful to have worked with such a productive team of citizen and professional scientists for the past 5.5 years.
To-date, we have made over 2.27 million classifications and published 10 refereed journal articles. We have another 1 submitted and another to be submitted in the next few weeks.
Looking towards the future, we are of course in the process of developing the next-generation of Radio Galaxy Zoo projects. For that, we ask that you stay tune for our future announcements of the suite of Radio Galaxy Zoo 2 projects that we are planning to launch. Of course, we will be keeping you all informed about our latest RGZ-based follow-up observations (e.g. the Zoo Gems programme with the Hubble Space Telescope). Therefore, this is not the last message from us.
To cap-off this impending retirement, I propose that we make a final RGZ sprint to the finish in the remaining days April 2019 –that is, let’s all try to classify as many sources as we can in the next few weeks!
Thank you very much again and let’s all make a concerted push to the finish line!
Ivy & Stas
The following blogpost is from Avery Garon who led the publication of Radio Galaxy Zoo’s latest science result. Congratulations to Avery and team!
Radio Galaxy Zoo is starting the new year strong, with another paper just accepted for publication. “Radio Galaxy Zoo: The Distortion of Radio Galaxies by Galaxy Clusters” will appear soon in The Astronomical Journal and is available now as a pre-print on the arXiv: https://arxiv.org/abs/1901.05480. This paper was led by University of Minnesota graduate student Avery Garon and investigates several ways in which the shape of a galaxy’s radio emission is affected by and informs us about the environment in which we find the galaxy.
Like the previous RGZ paper, we are looking for how the radio tails extend into the hot plasma that fills galaxy clusters (the intracluster medium, or ICM). This time, we measure how much the two tails deviate from a straight line, marked in the example below by the value θ. The standard model is that the ICM exerts ram pressure on the galaxy as it travels though the cluster and causes its tails to bend away from the direction of motion. However, while individual clusters have been studied in great detail, no one has had a large enough sample of radio galaxies to statistically validate this model. Thanks to RGZ, we were able to observe the effect of ram pressure as a trend for the bending angle θ to increase for galaxies closer to the center of clusters (where the ICM density is higher) and in higher mass clusters (where the galaxies orbit with higher speeds).
Because ram pressure causes the tails to bend away from the direction in which the galaxy is travelling, we can use this knowledge to map out the kinds of orbits that these galaxies are on. Unlike planetary orbits, which are nearly circular and all in the same plane, the orbits of galaxies in clusters tend to be randomly distributed in orientation and eccentricity. Our sample of bent radio galaxies shows an even more striking result: they are preferentially found in highly radial orbits that plunge through the center of their clusters, which suggests that they are being bent as their orbits take them through the dense central regions.
Finally, we looked at radio galaxies that were far from clusters. Even though the median bending angle is 0° away from clusters, there is still a small fraction of highly bent galaxies out there. By counting the number of optical galaxies that are near the radio galaxies, we observed a sharp increase in the number of companions within a few hundred kiloparsecs of our bent radio galaxies. This suggests that even outside of true cluster environments, we are still observing bending induced by local overdensities in the intergalactic medium.
We’ve won a prize! The Royal Astronomical Society has given the Galaxy Zoo team – including the volunteers who have made the project the success it is – their Group Achievement Award for 2019. I will post the citation below, but mostly I’m delighted that this award recognises all those who have worked to make Galaxy Zoo a success.
Looking at the list of previous winners – the last two are the team behind ESA’s Planck satellite and the team who made the Nobel winning discovery of gravitational waves – is humbling, so this is really something to be proud of.
We’ll make plans to make sure everyone can celebrate the award when it’s presented at the National Astronomy Meeting later in the year.
Citation for the 2019 RAS Group Achievement Award (A)
The 2019 Group Achievement Award is awarded to the Galaxy Zoo team. With over ten years of engagement under their belt, the Galaxy Zoo team have contributed significantly to our knowledge of the formation and evolution of galaxies, through strong commitment to collaboration with members of the public. They have established citizen science as a standard mode of data analysis across astrophysics, and initiated new areas of research sparked by Galaxy Zoo discoveries. Their roughly 55 papers, ranging from studies disentangling morphology, environment and colour, through to studies of individual morphological characteristics, have been enabled by the team’s careful work to create catalogues and measure systematic effects inherent in the classification, before releasing the data to the community.
The Galaxy Zoo project has also inspired many similar projects across astrophysics and beyond, through the Zooniverse platform. Perhaps Galaxy Zoo’s most notable achievement is immensely effective outreach: the more than 500,000 people who have contributed to date come from a wide range of backgrounds, making participation in scientific research possible for all. Galaxy Zoo inspires and informs, and does so on an unprecedented scale. For these reasons, the Galaxy Zoo team is awarded the Group Achievement Award.