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Galaxy Zoo Mobile

Hi, I’m Lauren, a summer work experience student working with the Galaxy Zoo team at the University of Oxford for a couple of weeks, and it’s my pleasure to be able to bring you some fantastic news. Today, we’re launching the mobile version of Galaxy Zoo! Unlike the website version, this brand-new native mobile version has  questions with only two possible answers – just swipe left or right depending on your answer! This can create a more captivating and faster-paced experience when you are classifying galaxies.

Not only does this introduce a new and engaging platform for the project, but it also means that you can classify galaxies anywhere – on the bus, at the beach, at a concert, in the waiting room at the dentist etc. Hopefully, this will mean many more galaxy classifications whilst also providing easier access for our wide range of volunteers across the world. By introducing this app, we hope to inspire others to join our Galaxy Zoo team, no matter their qualifications or skill set.

Get involved by downloading the Zooniverse app (if you don’t have it already), heading over to ‘Space’ section, and selecting the ‘Galaxy Zoo Mobile’ project. From there, you will be greeted with three different workflows – ‘Smooth or Featured’, ‘Spiral Arms’ or ‘Merging/Disturbed’. Pick whichever you like! The simple, swiping interface allows you to classify galaxies much faster than ever before, meaning the Galaxy Zoo science team can produce results even quicker. So, download the Zooniverse app today and start classifying!

Apple App Store: https://apps.apple.com/us/app/zooniverse/id1194130243

Google Play Store: https://play.google.com/store/apps/details?id=com.zooniversemobile&hl=en

Happy classifying,

Lauren & the Galaxy Zoo Team

 

Supermassive Black Holes in Merging Galaxies

The following is a blog by Yjan Gordon (@YjanGordon), a postdoc at the University of Manitoba, Canada (having recently completed a PhD at the University of Hull). Here, he describes his new paper making use of the latest Galaxy Zoo classifications.

One of the key questions I look to address in my research is that of why the black holes at the centres of some galaxies are actively feeding on matter (an active galactic nucleus, AGN for short) and why some aren’t. We know of multiple mechanisms that can trigger an AGN, from high-impact galaxy mergers to secular processes such as feeding on the matter ejected from stars over the course of their lives. However, not all AGN are created equal, and many of these objects, whilst active, are only barely so. While more powerful AGN are having a steak dinner, these weaker variants are merely snacking.

The processes that initiate these weak AGN may be different to those that fuel their more powerful cousins or simply a scaled down version of the same mechanisms. For example, we know that the collision of two similar sized galaxies (known as a major merger) can trigger an AGN. Then a minor merger, where a small galaxy collides with a much more massive one, may provide less fuel for an AGN, resulting in one of these weak AGN. This is exactly the question we investigate in our latest paper.

In order to test whether minor mergers are a factor in triggering weak AGN, high quality, deep observations are needed to look for very faint merger signatures in a sample of these galaxies. To conduct our analysis we made use of the Dark Energy Camera Legacy Survey (DECaLS). This survey not only provides the deep, high quality imaging necessary for looking for minor galactic mergers (and is far improved in this regard than previous wide-field imaging surveys, see figure below), but is also the latest survey being put to the galaxy zoo volunteers to obtain reliable galaxy morphologies.

Comparison of imaging from the Sloan Digital Sky Survey (SDSS, top) with higher quality imaging from DECaLS (bottom). The DECaLS imaging is approximately two magnitudes deeper than the SDSS imaging and shows faint merger remnants not visible in the SDSS images.

A control sample of galaxies that don’t host an AGN is required, so that we can compare the fractions of weak AGN and non-AGN experiencing mergers, i.e. are mergers more frequently associated with these AGN or not? In order to control against other variables that could impact your results here, reliable morphological information is a valuable asset. For instance, spiral galaxies have a delicate structure that can be disrupted by galaxy mergers, and the presence of this morphology in a merging system can provide information about the scale or timeline of the event. One can hence see the potential for elliptical galaxies to be more likely to exhibit the tidal disturbances than their more delicate spiral counterparts.

This kind of project wouldn’t be possible without the contributions of the many Galaxy Zoo volunteers providing morphological classifications on hundreds of thousands of galaxies.

When we compare the merger rates and the merger scales in both the weak AGN and the non-AGN control sample we found a couple of compelling results.

Firstly, we found that the fraction of both these samples experiencing minor mergers was about the same. This is interesting as it shows that minor mergers, which had long thought to be a potential trigger for these weak AGN, are not involved initiating weak activity of the central black hole in a galaxy.

Secondly, we found that for the least massive of these weak AGN, major mergers were significantly more common than in non-AGN. This is an unexpected result, as such major mergers might provide so much gas that any resulting AGN might be expected to be fairly powerful. Furthermore, previous research hadn’t shown any substantial evidence of this, so why are we seeing such an effect? Well, whilst major mergers are more common in these weak AGN, they still only represent a minority of the weak AGN population (~10%), and are thus not typical of the main population of weak AGN. One intriguing possibility is that these particular objects may actually be the early stages of more powerful AGN, and that as the merger progresses, and more gas falls into the galactic nucleus, the AGN will have more fuel to feed on and become a more powerful AGN. Further research is required to investigate such a hypothesis.

This kind of project wouldn’t be possible without the contributions of the many Galaxy Zoo volunteers providing morphological classifications on hundreds of thousands of galaxies. In this case, as is so frequent in research, not only have we answered a question about the evolution of these galaxies, but we have been presented with another.

Please keep up the great work, it really makes a difference.

Enhancing Galaxy Zoo

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.

Chris

Machine Learning Messaging Experiment

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).

Galaxy Zoo Upgrade: Better Galaxies, Better Science

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 right), and somewhat more galaxies which are diverse, interesting and unusual (like the ones on the left). 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 left, and somewhat fewer like the ones on the right.

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.

Scaling Galaxy Zoo with Bayesian Neural Networks

This is a technical overview of our recent paper (Walmsley 2019) aimed at astronomers. If you’d like an introduction to how machine learning improves Galaxy Zoo, check out this blog.

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.

Galaxies/day required to keep pace with upcoming surveys now, by 2019 year-end, and by 2022 year-end. Estimates from internal science plan.

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:

  1. They account for varying uncertainty when learning from volunteer responses
  2. 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.

Active Learning

Modern surveys will image hundreds of millions of galaxies – more than we can show to volunteers. Given that, which galaxies should we classify with volunteers, and which by our Bayesian CNN?

Ideally we would only show volunteers the images that the model would find most informative. The model should be able to ask – hey, these galaxies would be really helpful to learn from– can you label them for me please? Then the humans would label them and the model would retrain. 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).

We can use our posteriors to work out which galaxies are most informative. Remember that we use dropout to approximate training many models (see above). We show in the paper that informative galaxies are galaxies where those models confidently disagree.

Informative galaxies are galaxies where the each model is confident (entropy H in the posterior from each model is low) but the average prediction over all the models is uncertain (entropy across all averaged posteriors is high). See the paper for more.

This is only possible because we think about labels probabilistically and approximate training many models.

What galaxies are informative? Exactly the galaxies you would intuitively expect.

  • The model strongly prefers diverse featured galaxies over ellipticals
  • For identifying bars, the model prefers galaxies which are better resolved (lower redshift)

This selection is completely automatic. Indeed, I didn’t realise the lower redshift preference until I looked at the images!

I’m excited to see what science can be done as we move from morphology catalogs of hundreds of thousands of galaxies to hundreds of millions. If you’d like to know more or you have any questions, get in touch in the comments or on Twitter (@mike_w_ai, @chrislintott, @yaringal).

Cheers,
Mike

Thanks for the millions!

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.

Cheers,
Ivy & Stas

Winding Problems

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.

Screen Shot 2019-04-25 at 14.39.54

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.

Screen Shot 2019-04-25 at 15.01.26

The Hubble Tuning Fork illustrated with SDSS images of nearby galaxies.

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.

Screen Shot 2019-04-25 at 15.15.13

Figure 5 from Masters et al. (2019) paper.

 

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. 


 

If you’re inspired to keep classifying, head over to the main Galaxy Zoo project, or why not draw a few spiral arms over at Galaxy Zoo: 3D where we’re trying to understand spiral arms in more detail.

 

Radio Galaxy Zoo final sprint !

Screen Shot 2015-10-24 at 11.11.24 PM

Radio Galaxy Zoo logo

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!

Cheers,
Ivy & Stas