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.
Happy 5th birthday to Radio Galaxy Zoo!
We have now completed 84% of the project and reached 2.24 million classifications (the equivalent of ~90.2 years of work) thanks to all the hard work from our Radio Galaxy Zooites. So much has happened in the world of Radio Galaxy Zoo this year and many of the new scientific results we reported cannot have happened without your help.
In 2018, we had 4 papers accepted for publication in the Monthly Notices of the Royal Astronomical Society, doubling the number of papers that Radio Galaxy Zoo previously published. In addition, we have three more Radio Galaxy Zoo papers that have been submitted this year and are currently undergoing the refereeing process.
As always, our science papers can be freely-accessed and so I encourage you all to check out the following papers if you are interested. Here is the list of papers published this year:
1) Radio Galaxy Zoo: compact and extended radio source classification with deep learning by Vesna Lukic et al
2) Radio Galaxy Zoo: machine learning for radio source host galaxy cross-identification by Matthew Alger et al
3) Radio Galaxy Zoo: CLARAN – a deep learning classifier for radio morphologies
by Chen Wu et al
4) Radio Galaxy Zoo: observational evidence for environment as the cause of radio source asymmetry by Payton Rodman et al
As we summarise the main events this year, it would be remiss of me to not mention the retirement of our previous co-Primary Investigator (co-PI) as well as original driver of this project, Dr Julie Banfield, without whom Radio Galaxy Zoo wouldn’t be where it is today. We continue to be very grateful for her hard work and support. Finally, I would like to thank Dr Stas Shabala for agreeing to be a co-PI on this project after Julie’s departure for greener pastures.
Thank you all very much again for all your help and we shall continue to report on the science that is made possible thanks to you all. Keep up the awesome work! We hope that you all have a happy end-of-2018 and an excellent 2019.
Ivy & Stas
One of the most enduring serendipitous finds of the original Galaxy Zoo was a category of giant gas clouds shining from the energy input of active galactic nuclei (AGN) which have since faded (being a little cavalier here with time and verb tenses, since we can’t get news faster than light travels). The most famous of the is of course Hanny’s Voorwerp, whose discovery led to subprojects which turned up many more (“Voorwerpjes”). We have new results now on a related project going back to the Galaxy Zoo Forum, where we searched for gas in companions to active galaxies which is ionized by the AGN, and therefore gives us one more way to learn about how bright the AGN was tens of thousands of years before our direct view. Read More…
Radio Galaxy Zoo: what radio lobe shapes tell us about the mutual impact of jets and intergalactic gas
The following blogpost is from Stas Shabala about the Radio Galaxy Zoo paper led by his student, Payton Rodman, exploring the origin of asymmetries observed in a sample of Radio Galaxy Zoo radio galaxies.
Another Radio Galaxy Zoo paper has just been accepted for publication. “Radio Galaxy Zoo: Observational evidence for environment as the cause of radio source asymmetry” will shortly appear in Monthly Notices of the Royal Astronomical Society, and is already available on the preprint server (https://arxiv.org/abs/1811.03726). This paper, led by University of Tasmania undergraduate student Payton Rodman, looks at the properties of lobes in powerful radio galaxies. These lobes are inflated by a pair of jets, emerging in opposite directions from the accretion disk of the black hole at the centre of their host galaxy. Astronomers have known for a while that how big, bright or wide the radio lobes are depends on the properties of the intergalactic gas into which these lobes expand. Small, slow-growing lobes are usually found in galaxy clusters, while their large, rapidly expanding cousins tend to stay away from such dense environments. Radio lobes move about and heat intergalactic gas, and in this way they are thought to be responsible for regulating the formation of stars (by staving off the gravitational collapse of cold gas) in massive galaxies over the last eight billion years. Because of this, understanding how jets and lobes interact with their surroundings is important for understanding the history of the Universe. What complicates matters is that the mechanisms responsible for feeding the black hole and generating jets are also different in these two environments. So does nature or nurture determine what the lobes look like?
We decided to use the fact that all radio galaxies start out with two intrinsically identical jets propagating in opposite directions. If the two resultant lobes look different, this could only be due to the interaction with the surrounding gas – in other words, nurture. To test the nurture hypothesis, we used the first tranche of Radio Galaxy Zoo classifications. We selected all sources classified by citizen scientists to contain two clear radio lobes, and subjected this sample to a number of rigorous cuts on brightness, shape, redshift, and availability of environment information. Hot intergalactic gas is usually traced by X-ray observations, but these are unavailable for the majority of the sample. Instead, we used the clustering of optical galaxies from the Sloan Digital Sky Survey, which should be a good proxy for the underlying gas distribution. Then, for each radio galaxy, we compared the properties of the two radio lobes to how many galaxies were found near each of the lobes. We found a clear anti-correlation between the length of the radio lobe, and the number of nearby galaxies – in other words, shorter lobes have more galaxies surrounding them. These results were in excellent agreement with quantitative predictions from models (such as this hydrodynamic simulation made on the University of Tasmania’s supercomputer by PhD student Patrick Yates), which show that it is more difficult for lobes to expand into dense environments. The relationship between the luminosity of the lobes and galaxy clustering was much less clear, again consistent with models which predict a highly non-linear luminosity evolution as the lobes grow.
The excellent agreement between models and observations suggests that it is nurture, not nature, which determines lobe properties. It also opens up a new way of studying radio galaxy environments: though sensitive observations of optical galaxy clustering. With help from Zooites, we hope to expand this work to a much larger Radio Galaxy Zoo sample, which would allow us to probe the finer aspects of jet – environment interaction. Further afield, the ongoing GAMA Legacy ATCA Southern Survey (GLASS) project on the Australia Telescope Compact Array, as well as the Australian Square Kilometre Array Pathfinder EMU survey, will use this method to study the physics of black hole jets and the impact they have on their surroundings in a younger Universe.
On the 31 October 2018, Radio Galaxy Zoo published its first end-to-end machine learning system for “Classifying Radio sources Automatically using Neural networks” (ClaRAN). This paper is led by ClaRAN’s developer, Chen Wu, a data scientist at the International Centre for Radio Astronomy Research at the University of Western Australia (ICRAR/UWA), who repurposed the FAST-rCNN algorithm (used by Microsoft and Facebook) to classify radio galaxies. ClaRAN was trained on radio galaxies classified by Radio Galaxy Zoo and so recognises some of the most common radio morphologies that have been classified.
The purpose of ClaRAN is to reduce the number of radio sources that require human visual classification so that future Radio Galaxy Zoo projects will have fewer “boring” sources, thereby increasing the chances of real discoveries by citizen scientists. ClaRAN (and its future cousins) are crucial for future surveys such as the EMU survey which is expected to detect ~70 million radio sources (using the Australian Square Kilometre Array Pathfinder telescope). While Radio Galaxy Zoo has made visual source classifications much more efficient, we will still need to reduce the total survey sample size to a sample for visual inspection that is less than 1% of the 70 million sources.
How does ClaRAN work? ClaRAN inspects both the radio and coordinate-matched infrared overlay in the same fashion as RGZ Zooites, and then determines the radio source component associations in a similar fashion to the RGZ Data Release 1 (DR1) catalogue. As ClaRAN is still in its prototype stage (–analogous to the capabilities of a toddler), it only understands 3 main classes of radio morphologies — sources which have 1-, 2- or 3- separate radio components. ClaRAN was trained to understand these three different radio morphologies through seeing examples of all three classes from the RGZ DR1 catalogue. The animated gif (from the ICRAR press release) describes how ClaRAN “sees” the example radio galaxy. Please do not click on the link to the animated gif if you suffer from epilepsy or have any issues with flashing images.
As we look towards the future, we look forward to teaching ClaRAN some of
the more complex and exotic radio galaxy structures. For that to happen, we need to assemble much larger samples of more complex radio morphology classifications. With your support of Radio Galaxy Zoo, I am sure that we will get there.
Fun fact: did you know that some of the more obscure bugs in the RGZ DR1 catalogue processing was actually found through training ClaRAN? This is because ClaRAN is a good learner and will learn all the small details that we didn’t initially notice. We only discovered these bugs through some of the funny answers that we got out of some of the early testing of ClaRAN.
Thank you very much again to all our Radio Galaxy Zooites for your support. More information on the ICRAR press release for ClaRAN can be found via this link: https://www.icrar.org/claran/
Just a quick post to say thank you for your contributions to Galaxy Zoo: 3D in the last couple of weeks. I’m delighted to say that the bar drawing task is now completed. We still have a lot of spirals to draw though, so if you are ready for a challenge come join us in drawing these beautiful structures. Remember we collect 15 answers per galaxy, and use clever algorithms to combine them into a really reliable answer – so do your best, but don’t get too worried if your hand slips slightly! 🙂
I’m posting this on behalf of Amelia Frasier-McKelvie, a postdoc at the University of Nottingham, UK.
Hi – I’m Amelia, and I’m a postdoctoral researcher at the University of Nottingham. I work on galaxy evolution using the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey (and yes, I know it’s a contrived acronym!) MaNGA aims to observe 10,000 galaxies using integral field spectroscopy, which means instead of just obtaining one spectrum of a galaxy, we take several hundred at all different points across a galaxy. From this, we can infer interesting spatial information on galaxy properties. For example, we can see the regions in which star formation is occurring, or compare the ages of the stars in the bulge regions of a galaxy to the outer disk. By breaking down a galaxy into its components (such as bulge, disk, spiral arms, and bar) we can discover more about how the galaxy formed, and how it has led its life so far.
I’m really interested in how bars affect their host galaxies. In particular, I’m looking for observational evidence that bars are involved in the quenching of star formation within a galaxy. This phenomenon is known as secular evolution/quenching. The one thing a galaxy needs to form stars is a reservoir of cool hydrogen gas. It’s been postulated that a bar can transfer matter (such as gas) radially inwards through a galaxy’s disk and into its central regions. If this is the case, then maybe it takes this gas required for star formation and funnels it towards the galactic centre, starving the disk, and ceasing star formation. Simultaneously, this gas that is funnelled into the central regions could either induce a final starburst, using up all gas, or feed an active galactic nucleus (AGN), which can heat the gas to a point where it cannot collapse to form stars.
If we wanted to catch a bar in the act of quenching a galaxy, we could look for tell-tale signs of this funnelling action, namely a difference in the age and chemical composition of the stellar populations in the bar region of a galaxy when compared to the disk.
This is where Galaxy Zoo 3D comes in — I need to know where the bars actually lie in their host galaxies! Galaxy Zoo 3D citizen scientists mark out the bar (and spiral arm) regions of the MaNGA galaxy sample, which I can apply to our data cubes and extract spectra belonging to the galactic bar and disk regions. I can then analyse the bar and disk spectra separately and compare their properties. I’m interested in the global properties of a large sample of galaxies, so I need as many bar and disk region classifications as possible!
If I can find observational evidence that bars are helping to quench galaxies this will confirm the idea that internal secular evolutionary processes are important in galaxy evolution.
This will prove that along with external factors, internal structures such as bars are extremely important in determining a galaxy’s fate!
We hope you enjoyed hearing about how the masks made in Galaxy Zoo: 3D are being used. There’s still plenty of bars and lots and lots of spirals to mark in the project, so please join in if you’d like to help us complete our sample and help Amelia and others (including me!) with their research.
Some of you may remember a while back we posted a blog announcing that we would be testing a new messaging system on Galaxy Zoo. Some of you may even have seen these messages while classifying on the site! This test was also part of a study of how we could use messaging to increase engagement on the project. Working with researchers from Ben Gurion University and Microsoft Research we delivered messages to volunteers at key times during their participation on Galaxy Zoo and observed how these messages affected their engagement. This research was based on previous work we had done that demonstrated that sending similar messages in emails could increase the likelihood of volunteers returning and engaging more with the project.
The volunteers were split into three main cohorts; One group who were delivered the messages at random intervals, one group who were delivered the messages at what were predicted to be optimal times, and a final control group who received no messages. This study has led to two peer-reviewed publications and the results show that optimal timing of an intervention message can significantly increase the engagement of volunteers on Galaxy Zoo.
These early results are intriguing, and we’d like to do more tests to see if it’s something we can use more broadly across Zooniverse projects. The same machinery might also be used by Zooniverse teams to send messages to volunteers – either in a group or individually – as they participate in their projects. We’ll keep you informed on the blog.
To read about the study and its finding in more detail please see the following papers:
For a discussion regarding the ethics of this study, please read this Zooniverse Talk thread https://www.zooniverse.org/talk/14/675633.
For the past few years, a new Galaxy Zoo project has been under development. This project allows the creation of models of galaxies inside the Zooniverse website (although in a slightly experimental fashion). Many of you helped trial this project in December of 2017, and some have classified since it was quietly launched in late April. I’d like to take this opportunity to share with you some of the early results we have obtained from your classifications!
From the beta
260 of you helped beta test and debug the project in our beta, providing invaluable feedback (most of which we hope we addressed!) and submitting over one thousand test classifications of our small test sample. One of the images in this sample was the SDSS r-band image of the galaxy below, catchily known as SDSS J104238.12+235706.8.
In this post we’ll look specifically at the spiral arms drawn on this galaxy, and how we can recover information about the shape of the spirals from your drawn arms. If we plot every spiral arm drawn on the galaxy, two distinct spiral appear:
There are a number of classifications which have either crossed the center of the galaxy, linking the two arms with one line, or which attempt to enclose the spiral (as in the GZ:3D project) rather than tracing along the center line. This confusion arose from the short tutorial and confusing help text present in the beta, which was flagged by our testers (thank you!).
We can make use of unsupervised machine learning techniques to cluster together these drawn arms, and extract points corresponding to each arm (while throwing away some lines which didn’t fit into our groups).
Taking one arm as an example, we identify points which could be considered outliers, and remove them to improve our later fit (using another unsupervised machine learning technique called Local Outlier Factor). In the image below, red dots correspond to points identified as outliers, and the blue contours can be seen as a probability map, with points in regions of darker blue being more likely to be an outlier.
Fitting a spiral
Brilliant – we now have a load of (unordered) points which roughly resemble the spiral arm of our galaxy! We’ll fit a smooth line to obtain a “best guess” of the galaxy’s spiral properties, the result of which can be seen below:
What we’ve discovered from the beta trial is that we need at least 20 attempts at drawing spirals on a galaxy to get reliable answers, so we’re keen to get more people trying to build galaxies on Galaxy Builder.
If you’d like to help please join us at https://www.zooniverse.org/projects/tingard/galaxy-builder. If that’s not your thing we still need classifications on Galaxy Zoo: 3D, and the classic Galaxy Zoo is still live with all new images from the DECaLS survey.