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Presenting results from the Galaxy Builder project

From April 2018 until early this year, Galaxy Builder has collected over 18,000 models of spiral galaxies, built by volunteers. These models were combined and computationally fine-tuned, and the results have been compiled into Lingard et al. 2020 recently accepted for publication in the Astrophysical Journal.

The project asked volunteers to sequentially add components to a galaxy, starting with the galaxy’s disc, then, if one is present, a bulge and a bar, followed by tracing any visible spiral arms. At each stage, light from the corresponding component would be removed until the whole galaxy was accounted for:

Four-panel figure showing the galaxy builder interface, a spiral galaxy is visible in blue, and in each panel another component is added to gradually remove all the visible light from the galaxy.

Four-panel figure showing the galaxy builder interface, a spiral galaxy is visible in blue, and in each panel another component is added to gradually remove all the visible light from the galaxy.

 

After collecting 30 volunteer models for each galaxy, we then used Machine Learning techniques to cluster components, and identify a “consensus model”.

Four panel plots showing the clusterd and consensus components for an example galaxy. There is a small amount of scatter in each component, but the clustering has reliably found a good result.

Four panel plots showing the clusterd and consensus components for an example galaxy. There is a small amount of scatter in each component, but the clustering has reliably found a good result.

We then used a computer fitting algorithm to fine-tune this model, resulting in a detailed description of the galaxy’s light distribution, which we can use to understand the physical processes occurring inside it!

Five panel plot showing an image of the example galaxy, the fitted model (which matches the real galaxy very well), the difference between the galaxy and model (which is small), and how the consensus components from clustering have changed during fitting (they do not change very much)

Five panel plot showing an image of the example galaxy, the fitted model (which matches the real galaxy very well), the difference between the galaxy and model (which is small), and how the consensus components from clustering have changed during fitting (they do not change very much)

We have shown that galaxy model created in this way are just as reliable as simpler models obtained purely through computer fitting (when those simple models are appropriate!), by comparing to other published work and by incorporating a small sample of synthetic galaxies, for which we know the true light profiles:

The nine synthetic galaxy images, each of which look very realistic (but without clumpy star-forming regions). Most have spiral arms, and some have bars.

The nine synthetic galaxy images, each of which look very realistic (but without clumpy star-forming regions). Most have spiral arms, and some have bars.

For most parameters, the difference between the true (x-axis) and volunteer-provided (y-axis) values is tiny. There are some issues with bar “boxyness” and bulge concentration (Sérsic index), primarily due to the computer fitting algorithm not being able to distinguish between different combinations of values:

Scatter plots showing how well parameters are recovered. We see that the method generally does a very good job, but there is a lot of scatter in bulge sersic index and bar boxyness.

Scatter plots showing how well parameters are recovered. We see that the method generally does a very good job, but there is a lot of scatter in bulge sersic index and bar boxyness.

Thanks to the added complexity of Galaxy Builder models, we have a much richer source of information for scientists to delve into! We’re excited to share the scientific results we’ve obtained, expect another post in the not-too-distant future (hint, spiral arms are complicated)!

The research team want to send a very warm thank-you to everyone who has participated in this project over the years. Without your efforts we would not have had the chance to do the science we are so passionate about, and we are very excited for the future of the Zooniverse.

Galaxy Builder is currently finished collected classifications, but we still need your classifications in Galaxy Zoo, where we’re working on collecting classifications for images from the DECaLs survey.

This blog was posted on behalf of Tim Lingard for the Galaxy Builder Team. Tim also submitted his PhD thesis based on this work this summer, and is now working as a Data Analyst for the 1715 Labs

New Paper: Morphological Conformity in Galaxy Zoo

I’m delighted to report on the publication of the below paper, led by Justin Otter, a talented recent graduate of Haverford College who worked with me on this project since their Junior year (September 2017). Link to MNRAS abstract. Link to Arxiv version of accepted paper.

 

In this paper we used data from Galaxy Zoo 2 to investigate the idea of galactic “conformity” in galaxy morphologies.

What is Galactic Conformity?

Great question. It’s a fairly simple idea actually – the idea that satellite galaxies in groups are more likely to have similar properties to their central galaxy than a random galaxy in the Universe would. The satellites “conform” to the properties of the largest (central) galaxy in the group. So in a group with a star-forming central galaxy would be more likely to have star-forming satellite galaxies than a group with a passive (not star-forming) central galaxy.

What’s New in this Paper? 

The new thing here is looking at the star-formation and morphological properties of galaxies separately. So this comes right back to our discovery with the help of all of you at Galaxy Zoo (almost 10 years ago now!) that not all spirals are star-forming (blue), and not all ellipticals are passive (red).

Credit: Otter et al. 2020 (courtesy KLM)

Most studies looking into galactic conformity prior to our work had used star-formation properties to find the signal, and then interpreted the result as if it was a morphological change in the galaxies. This is obviously an over-simplification, and we (well actually it was Brooke who said it first!) wondered if this was driven by star-formation conformity or morphological conformity – and could you find the signal separately for the two.

So what did you find? 

Well we found conformity in both star formation and morphological type, but that the signal was stronger in star-formation properties. We also looked at satellites around red/passive and blue/star-forming spiral and elliptical central galaxies separately, and found the signal was largest around the red/passive elliptical central galaxies.

We made the following cartoon version of what we observed (note this is highly exaggerated – in all cases it was a small excess probability, not that all satellites around a central share its properties). All shapes of galaxies showed star-forming conformity, but there was only morphological conformity around ellipticals and blue spirals, suggesting that satellites of red spirals may have turned into ellipticals before the central galaxy

Credit: Justin Otter. A cartoon version of our observations

What does this mean for galaxy evolution? 

This all fits quite nicely into the picture where star-formation properties of galaxies can change quite easily, but it’s much harder to change morphology, so as a group evolves and/or accretes more satellites they are likely to change star-forming properties more easilly/quickly than the morphological properties.

So thanks again for the classifications which give us the data to do this work!

In case you’re curious, Justin is currently working as a Fulbright Scholar at the Max Planck Institute for Astronomy in Heidelberg, Germany, and will return to the US for the next academic year to begin his PhD at John’s Hopkins University.

Galaxy Zoo + Galaxy Zoo: 3D

Hi! I’m Tom, and I’m a PhD student at the University of Nottingham, doing some research to try to understand how spiral galaxies have grown and changed over their lifetimes. I’m especially interested in looking at how the spiral arms have been affecting the galaxy as a whole. I’ve recently finished up a paper in MNRAS in which I’ve been demonstrating a couple of new methods using some Galaxy Zoo data.

Amelia has already written [ https://blog.galaxyzoo.org/2018/07/17/finding-bars-in-galaxy-zoo-3d/ ] about how she is using the MaNGA survey [ https://www.sdss.org/surveys/manga/ ] to try to understand what’s happening in bars, so I won’t go into too much detail about this fantastic survey. I’ll just say that it’s part of the Sloan Digital Sky Survey, and for each of its sample of 10,000 galaxies, we have measurements of the spectrum at every position across the face of the galaxy.

MaNGA is really useful for trying to understand how galaxies have grown to their current size, because it is possible to get some sort of estimation of what kinds of stars are present in different locations of the galaxy. It’s a difficult thing to measure, so we can’t say exactly how many of every different type of star is present, but we can at least get a broad picture of the kinds of stellar ages and chemical enrichment (“metallicity”) in the stars. Astronomers have used these kinds of tools to measure the average age or metallicity of stars in different parts of galaxies, and found that in most spirals, the further out you go in the galaxy, the younger the stars are on average. The usual interpretation of this is that bulges tend to have formed first, and the disks have grown in size over time afterwards.

A MaNGA spiral galaxy. We can obtain information about the kinds of stars residing across the hexagonal area, which helps us understand how they’ve grown and evolved.

I’m really interested in trying to push this picture in two ways. Firstly, I’ve been trying to see what we can learn from looking at the general distribution of stars of different ages and metallicities – not just the average properties – at each location in the galaxy. Secondly, I think there is a lot of information that we risk ignoring by only looking at how things change with galactic radius. Spiral arms and the bar aren’t evenly distributed around the galaxy, so if we can see how the stellar properties change as we move around the galaxy, we should be able to measure what effect the spiral arms and bars have on the stars. The goal would be to try to confirm whether the most popular models of the nature of spiral arms and bars are correct or not.

To properly do this, we need to know exactly where the spiral arms and bars are in the MaNGA galaxies, so that we can see how the stars vary in these different regions. Enter Galaxy Zoo: 3D, where volunteers are asked to tell us where the different components are.

An example galaxy in MaNGA, where we’ve managed to split the galaxy into different stellar populations of different ages. Each frame shows where we find stars of a given age in this galaxy, starting from the oldest stars and finishing with the most recently formed stars. The colour denotes the mean metallicity of the stars, shown by the scale at the bottom.

All of this is what my most recent publication is about (read it in full at https://doi.org/10.1093/mnras/stz2204); we’ve shown that by combining the full spatial information available from MaNGA (augmented by Galaxy Zoo:3D) with the full distributions of the ages and metallicities of stars in each location, we can start to see some interesting things in the bar and spiral arms. It’s definitely best illustrated by an animation.

By splitting the age distributions up into different “time-slices”, we can create images of where stars of different ages are located in each of our MaNGA galaxies. Immediately from this one example, it’s obvious that there’s a lot of things going on here.

There are a few features in the animation that we’re not entirely convinced are real, but the main exciting things are that the spiral arms only show up in the youngest stars, and the bar grows and rotates as we move from older to younger stars. The growth of the bar is intriguing; this might be showing us how it formed. The bar changing with angle is even more exciting, and we think it shows us how quickly new-born stars become mixed and “locked” into the bar. The arms show what we should expect; spiral arms are areas of intense star formation, but over time the stars formed there will become mixed around the disk. We measured this effect by looking at what fraction of stars of each age are located in the volunteer-drawn spiral arms from Galaxy Zoo:3D.

This is really interesting, and highlights the power of combining large surveys like MaNGA with crowd-sourced information from the Zooniverse.

The next step is to do these kinds of things with more than just this one galaxy though. I’ve started looking at how these techniques can measure how fast the disks of spiral galaxies grew, using a large sample of spiral galaxies identified by Galaxy Zoo 2 volunteers. I’m also trying to measure how quickly stars get mixed away from spiral arms in different types of spiral galaxies. I have started to find some hints of some exciting results on both of these topics, which I would love to share in a future blog post if you’re interested.

We need volunteers to tell us where the spiral arms and bars are in galaxies, so that we can start to see what makes these regions special.

However, I’m currently limited in the number of galaxies with spiral arm regions identified by Galaxy Zoo:3D volunteers, so it would be really helpful if we could get some more! Understanding what makes spiral structure appear in disky galaxies is one of the unsolved problems in galaxy evolution and formation, and the clues to finding out might well lie in measuring how spiral arms affect the galaxy’s stars. Galaxy Zoo:3D will definitely be able to play a role in this! Help us out at https://www.zooniverse.org/projects/klmasters/galaxy-zoo-3d.

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).

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 studies cluster environment impact on radio galaxy morphologies

The following blogpost is from Avery Garon who led the publication of Radio Galaxy Zoo’s latest science result. Congratulations to Avery and team!

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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).

avery_blogfig

Example source RGZ J080641.4+494629. The magenta arrows extend from the host galaxy identified by RGZ users and terminate at the peaks of the radio emission, defining the bending angle θ. The cyan arrow is used to define an orientation for the galaxy with respect to the cluster.

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.

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.

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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?

PLUTO_asymmSims_M25-Q38-R1_theta30

Still snapshot of hydrodynamic simulation of asymmetrical radio jets by Patrick Yates from the University of Tasmania. Check out the movie clip here

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.

Radio Galaxy Zoo’s ClaRAN

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/

 

 

 

 

Gems of the Galaxy Zoos: help pick Radio Galaxy Zoo Gems!

Help vote for Radio Galaxy Zoo Gems!

At the Galaxy Zoos (both at Galaxy Zoo & Radio Galaxy Zoo), we are fizzling with excitement as we prepare for observations using the Advanced Camera for Surveys instrument on board the Hubble Space Telescope. These new Hubble maps will have greater resolution than those that we have from the Sloan Digital Sky Server.

As mentioned by Bill’s blogpost, we have been allocated fewer observing slots than our full list of candidates. Therefore, we invite all of you to help shape the observing priorities of our current target list. You will help determine which host galaxies would gain the most from these Hubble observations (and thus have highest priorities on the target list).

The main science targets specific to these Hubble observations are the host galaxies of Green Double Radio-lobed Active Galactic Nuclei (Green DRAGN — pronounced Green Dragon) and Spiral Double
Radio-lobed Active Galactic Nuclei (S-DRAGN).

Figure 1: Example of a Green DRAGN that is also a Hybrid Morphology Radio Source (HyMoRS) found by Radio Galaxy Zoo (Kapinska, Terentev et al 2017)

Green DRAGN — The prominent green appearance in these DRAGN host galaxies come from the strong [OIII] emission line that dominate the emission in the Sloan r-band. Therefore, these galaxies appear very green in a Sloan 3-colour (g,r,i)  image due the lack of equivalently-strong emission in the Sloan g– and i– bands (the blue- and red- filters, respectively).  The Green Pea galaxies (Cardamone et al 2009) from the original Galaxy Zoo project are a class of green galaxies that appear to be dominated
by star formation. On the other hand, the Green Bean galaxies (Schirmer et al 2013) are thought to consists of quasar light echoes (eg Galaxy Zoo’s Hanny’s Voorwerp).  However, the original Green Bean population show little to no emission at radio wavelengths.

In Radio Galaxy Zoo, we have found a population of Green Bean-like galaxies which host bright radio lobes. Therefore, what sort of feedback are galaxies getting from these “radio-active” Green DRAGNs and how do they relate to the other green galaxies and our understanding of galaxy evolution? Figure 1 shows an example of a Green DRAGN that also happened to be a Hybrid Morphology Radio Galaxy
found by Radio Galaxy Zoo and published by team scientist Anna Kapinska in collaboration with citizen scientist Ivan Terentev (see blogpost on their paper).

Figure 2: An example S-DRAGN that is radio galaxy 0313-192 where VLA observations have been overlayed in red over an HST ACS image. (More details can be found in Bill’s paper: Keel et al 2006)

Spiral DRAGN — Typically, radio galaxies with big radio jets and lobes are hosted by early-type galaxies. Spiral galaxies are often thought to not be “mature” or massive enough to host giant radio lobes.  However, a few S-DRAGNs have been found in the past by our very own Bill Keel (Keel et al 2006, see Figure 2) and Minnie Mao (Mao et al 2015).  To shed light on this rare phenomena,
we seek your help through Radio Galaxy Zoo and this observing programme to assemble a more statistically significant number of this rare class of objects. Figure 2 shows a combined HST and VLA map of the S-DRAGN
published by Bill in 2006.

We have to finish this priority selection by the 16th February 2018. So, please help vote now by clicking hereWe have uploaded the targets in batches of 24 and so please click on all the batches for a view of the full target list.  A handy tip for inspecting these images is to ensure that your screen brightness is adjusted to its maximum because many of the host galaxy features can be very faint. 

We thank Radio Galaxy Zooites, Jean and Victor, for their immense help with assembling the priority selection project interface.  You can track what Hubble is observing by proceding to the Hubble archive link or the Hubble Legacy Archive interface here.

 

Gems of the Galaxy Zoos – help pick Hubble observations!

Galaxy Zoo and Radio Galaxy Zoo participants have an unusual opportunity to help shape a list of galaxies to be observed by the Hubble Space Telescope, as part of the “Gems of the Galaxy Zoos” project.

The project came about when the Space Telescope Science Institute circulated a message in August of 2017, seeking proposals for a new category of observation – gap-fillers. These projects will provide lists of target objects around the sky for brief observations when high-priority projects leave gaps in the telescope schedule, allowing 10-12 minutes of observation at intermediate places in the sky. Read More…