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New images for Galaxy Zoo from GAMA-KiDS!

Hello Zooniverse citizen scientists! We’re extremely excited to announce the release of a new dataset on Galaxy Zoo. For the past several months we’ve been working with scientific collaborators from the Galaxy And Mass Assembly Survey and the VST Kilo-Degree Survey. This blog post will give you a few details about these surveys, the new data set, and what we hope to achieve with Galaxy Zoo classifications.

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The Galaxy And Mass Assembly (GAMA) Survey is an international project to exploit the latest generation of ground and space-based survey facilities. Its aim is to study cosmology and galaxy formation and evolution from scales of thousands up to millions of light years across. The science goals include furthering our understanding of how the mass of stars within galaxies builds up over time, how and when do galaxies form their stars, how are those previous questions related to a galaxy’s environment, and at what epoch did star-formation and mass-build-up dominate? Visual morphologies from Galaxy Zoo will allow us to explore if, how, when, and where galaxies transition from one type into another, what impact this has on the formation of stars, and to look for new types of unique and interesting galaxies.

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The observations are from the Kilo-Degree Survey (KiDS) on the 2.6m VLT Survey Telescope (VST) located at the ESO Paranal Observatory in Chile. KiDS is a large optical imaging survey in the Southern sky designed to tackle some of the most fundamental questions of cosmology and galaxy formation of today. At the heart of KiDS lies the 300 million pixel camera OmegaCAM. Its instantaneous field of view is a full square degree and it was designed to provide extremely accurate measurements of the intrinsic shapes of faint, small galaxies.

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The 2.6m VLT Survey Telescope (VST), located at the ESO Paranal Observatory in Chile, is carrying out observations for the Kilo-Degree Survey (KiDS).

The scientific teams behind GAMA and KiDS have been working closely to put together this new set of images. Galaxies have been selected from a catalogue produced by the GAMA Survey and images have been constructed based on observations from KiDS. While some of these galaxies have already been looked at by Galaxy Zoo citizen scientists before using their Sloan Digital Sky Survey (SDSS) images, the improvement in the resolution and depth of KiDS images over SDSS imaging is remarkable. With this new GAMA-KiDS data set we hope to be able to study the very faintest structures within galaxies, as well as more accurately classify features which may have been missed before. Take a look at the image below to see how much clearer the new images are!

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This image compares SDSS images (on the left) with those from GAMA-KiDS (right) for three example galaxies: G107214, G298570 and G551505.  Our new images reveal a lot more detail!

We’re really excited about getting classifications for these new images, and we hope you are too! We’re more than happy to talk about any interesting galaxies you may come across and to answer any questions you may have. Until then, enjoy, and thank you for your help!

– by Dr Lee Kelvin, on behalf of the GAMA and KiDS teams

RGZ Team Spotlight: James Ansell

Hi everyone! I’m James and I’ve joined the RGZ team as a Communication/Engagement intern. I’m a PhD Candidate at the Australian National Centre for the Public Awareness of Science (CPAS) which is part of the Australian National University (ANU). I’m also a Sessional Academic (read: Tutor and marker) for a couple undergraduate courses covering things from ‘the Public Awareness of Science’ to ‘Science, Risk and Ethics’. And to pay the bills I work for the ANU in an administration role at (essentially) the Business School as well as a few other odd jobs.

But I am at heart an errant astronomer – having double majored in Astronomy/Astrophysics and Science Communications at the ANU for my B.Sci, graduating with Honours in 2015. I grew up in Alice Springs in the middle of Australia and had a purely spectacular night sky to look at. Something I only appreciated when I lived Brazil after graduating high school.

As part of my undergraduate studies I did dabbled a bit in some astronomy research. Firstly I did a project with Dr Charley Lineweaver (if you don’t know Charley, you should!) looking at the (surprisingly fuzzy) distinctions we make between objects in space e.g. planet, dwarf-planet, asteroid, moon. Let’s just say the project didn’t go where I thought it would.

Secondly, as part of an Astronomy Winter School I did research looking for ‘intergalactic stellar bridges’. Essentially chains of stars going from one galaxy to another which may have played a role in stellar formation in galaxies. I think. It was several years ago and the weather was against us when we went to do observations, so it didn’t go anywhere and my memory is pretty fuzzy on the details.

Outside of academia, I was involved in the ANU Black Hole Society (the Astronomy Club), the ANU Physics Society and the Science Communication Society. Also I absolutely love the TV series Cosmos, both the Carl Sagan original which I saw as a teenager and then the Neil deGrasse Tyson remake from a few years ago.

Since my astronomy research didn’t turn out particularly well, I ended up going down the science communication route. I’ve since done research looking into the effects of fictional doctors on young people’s perceptions of healthcare, factors affecting the uptake of vaccinations in Australia and the relationship between people’s perceptions of ‘Superfoods’ and their health behaviours. But I do miss the Astronomy and Astrophysics side of things so I’m super excited to be able to combine my two interests as part of the Radio Galaxy Zoo team.

(Also for some random fun facts about me – I used to host a music program on a Canberra community radio station, I founded the Canberra pop-culture festival ‘GAMMA.CON’ which is basically our local Comic-Con and I fly Hot Air Balloons with the ACT branch of the Scout Association.)

I’ll be hanging around in the forums under the name ‘JRAnsell’ and am keen to hear from you – if you’ve got questions about RGZ specifically or astronomy more broadly let me know! You can also hit me up on Twitter @radiogalaxyzoo or at radiogalaxyzoo@gmail.com.

There be S-DRAGNs!

This end-of-year post is written by Jean Tate, an RGZ citizen scientist and associate science team member who is providing us with the 2016 update on her team’s hunt for more Spiral Double Radio-lobe AGNs — SDRAGNs.   My warmest congratulations again to the SDRAGN Team!  I will be sure to look out for more SDRAGN news in 2017.  More information can be found at the SDRAGN team’s RadioTalk Discussion thread.

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A small band of intrepid scientists – citizen and regular – have been hunting SDRAGNs for quite some time now.  These strange beasts were mythical, until 1998 when one was spotted above the Antipodes (it goes by the highly memorable name of 0313-192 … not).  Since then a dozen or so other Spiral galaxies which host Double Radio lobes (and which have Active Galactic Nuclei; SDRAGN, get it?) have been bagged. With thousands of sharp-eyed citizen scientists, RGZ is an ideal place to look for more.

It has been relatively easy to find  SDRAGN candidates – two known ones were flagged by RGZooites, who were quite unaware of their status – but rather more challenging to turn candidates into certainties; for example, chance alignments can appear very convincing. Anyway, from ~a thousand “possibles”, the SDRAGN team picked ten really promising ones, and is now writing up a paper on them (actually, while doing some final checks, two of the ten turned out to be imposters; never mind, there are dozens more good candidates for a second paper).  Curiously, one of the most difficult questions was (and still is) “is this really a spiral?”

jtsdragnThe figure above shows J1649+26, an SDRAGN published by Minnie M. in 2015 (URL Link to her paper).  The red contours represent the double radio lobes emanating from the supermassive black hole of this galaxy.

You can see some of the SDRAGN candidates in RGZ Talk, by searching for the hashtag #SDRAGN (some will also have the hashtag #spiral; many candidates do not have either hashtag). If you find an SDRAGN candidate, please include the #SDRAGN hashtag in your comment.

Ferengi-2 Images Launched!

Hey volunteers! This is Mel G from the Minnesota science team, and I’m excited to announce the launch of the second set of FERENGI images on Galaxy Zoo today!

Some of you may remember classifying the first batch of FERENGI images back in 2013. For new volunteers, or experienced volunteers who need a refresher, FERENGI is a code that takes an image of a nearby galaxy and produces a new, simulated image of what that galaxy would look like if it was actually much farther away. 288 galaxies that were already classified by Galaxy Zoo volunteers were selected to be “ferengified” in that first sample; from these, 6,624 images were created of these galaxies at different distances and brightnesses. With your help, all images were classified and used to measure how distance affects classifications, which enabled us to debias and finally release the Galaxy Zoo: Hubble catalog just last month!

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original SDSS galaxy + 4 ferengified images at increasing distances

Recently, I found another use for these FERENGI classifications as I worked on my project on red disk galaxies, which will go into my PhD thesis to be completed this summer (coming up soon, yikes!). For this project I’ve been using data from Galaxy Zoo: Hubble to study the transformation of star-forming disk galaxies into non star-forming (aka “dead”) ellipticals between now and 6 billion years ago. Part of this research involves tracking the galaxy colors over time, which are indicators of whether the galaxies are still forming stars or not. A common way to distinguish star-forming galaxies from dead galaxies is to use a color-color diagram (this blog post goes into the details quite well, for the interested!). The short version is that galaxies in the upper-left of this plot, the “red sequence”, are no longer forming stars, and the lower-right portion, the “blue cloud”, are still producing lots of new stars. Typically the blue cloud is full of disk galaxies and the red sequence is full of ellipticals, but that statement is not 100% true;  there are actually quite a few disk galaxies mixed in with ellipticals up in the red sequence. We think these might represent a “transition” stage between blue/active disks and red/passive ellipticals, and studying how this population evolves with time will tell us more about how the shutting down of star formation is related to the morphological transformation.

What does this have to do with FERENGI? Well, detecting disk galaxies at high redshift is pretty hard – as we learned during the data reduction of the GZH catalog. Using raw Galaxy Zoo classifications, disks tend to be classified very similarly to ellipticals if they are very far away, so the number of disks we count is probably smaller than the true value. Using the FERENGI data, however, we can predict how many disks we should be detecting as a function of distance, and use that information to adjust the numbers of disks we count in the real Hubble data! The catch is that since galaxies with different colors tend to look a little different on average, it’s important to measure this incompleteness for both the red sequence and the blue cloud galaxies. Here comes the problem: in the original FERENGI sample,only 44 of the 300 galaxies have color data, leaving only 9 red sequence and 36 blue cloud galaxies to study. Unfortunately those numbers are too small to get a good measurement!

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color-color plots of the original and Ferengi-2 samples

So, as a sequel to the original FERENGI project, and as motivation to take proper measurements of red disks over time for my thesis, I’ve created FERENGI-2: a new set of FERENGI images from 936 galaxies. Each has been ferengified to 8 different distances, producing a total of 7,488 images that I need your help classifying. As you can see in the color-color plots here, these classifications will allow me to measure incompleteness for 388 galaxies in the red sequence (previously only 9) and 548 galaxies in the blue cloud (previously 36). This increase in data is huge, and will help not only the completion of my thesis, but many future projects that benefit from debiasing of Hubble data. Thanks again for your help!

Galaxy Zoo CANDELS

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We submitted the Galaxy Zoo CANDELS paper in May. Now, after some discussion with a very helpful referee, the paper is accepted! I hope our volunteers are as thrilled as I was to get the news. It happened within days of the Galaxy Zoo: Hubble paper acceptance. Hurray!

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Spot the typo! (No, just kidding.) (Well, sort of. There is one, but it’s not easy to find and it’s pretty inconsequential.) This is not quite the longest paper I’ve ever written, but it is the longest author list I’ve ever been at the top of. It includes both Galaxy Zoo and CANDELS scientists. And the volunteers are acknowledged too, in that first footnote. A lot of people did a lot of work to bring this together.

If you’d like to read the paper, it’s publicly available as a pre-print now and will be published at some point soon in the Monthly Notices of the Royal Astronomical Society. The pre-print version is the accepted version, so it should only differ from the eventual published paper by a tiny bit (I’m sure the proof editor will catch some typos and so on).

The paper may be a little long for a casual read, so here’s an overview:

  • We collected 2,149,206 classifications of 52,073 subjects, from 41,552 registered volunteers and 53,714 web browser sessions where the classifier didn’t log in. In the analysis we assumed each of those unique browser sessions was a separate volunteer.
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Most subjects have 40 classifications apiece, although some were retired early from active classification and others were classified further, until about 80 volunteers per galaxy had told us what they thought.

  • The raw consensus classifications are definitely useful, but we also weighted the classifications using a combination of “gold standard” data and consensus-based weighting. That is, classifiers were up- or down-weighted according to whether they could tell a galaxy apart from a star most of the time, and then the rest of the weighting proceeded in the same way it has for every other GZ dataset. No surprise: the majority of volunteers are excellent classifiers.
  • 6% of the raw classifications were from 86 classifiers who both classified a lot and gave the same answer (usually “star or artifact”) at least 98% of the time, no matter what images they saw. We have some bots, but they’re quite easy to spot.
  • Even with a pretty generous definition of what counts as “featured”, less than 15% of galaxies in the relatively young Universe that this data examines have clear signs of features. Most galaxies in the data set are relatively smooth and featureless.
  • Galaxy Zoo compares well with visual classifications of the same galaxies done by members of the CANDELS team, despite the fact that the comparison is sometimes hard because the questions they asked weren’t the same as what we did. This is, of course, a classic problem when comparing data sets of any kind: to some extent it’s always apples-vs-oranges, and the devil is in the details.
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We devote an entire section of the paper to comparing with the CANDELS-team classifications (from Kartaltepe et al. 2015, which we abbreviate to K15 in the paper). The bottom line: the classifications generally agree, and where they don’t we understand why. Sometimes it’s because there’s interesting science there, like mergers versus overlaps. The greyscale shading is a 2-D histogram; the difference in the blue versus red points is in which axis was used to separate the galaxy into bins so that the average trends could be computed.

  • By combining Galaxy Zoo classifications with multi-wavelength light profile fitting — where we fit a 2D equation to the distribution of light in a galaxy, the properties of which correlate pretty well with whether a galaxy has a strong disk component — we’ve identified a population of likely disk-dominated galaxies that also completely lack the features that are common in disk galaxies in the nearby, more evolved Universe. These disks don’t have spiral arms, they don’t have bars, they don’t have clumps. They’re smooth, but they are disks, not ellipticals. They tend to be a bit more compact than disk galaxies that do have features, even though they’re at the same luminosities. They’re also hard to identify using color alone (which echoes what we’ve seen in past Galaxy Zoo studies of various different kinds of galaxies). You really need both kinds of morphological information to reliably find these.
  • The data is available for download for those who would like to study it: data.galaxyzoo.org.

With the data releases of Galaxy Zoo: Hubble and Galaxy Zoo CANDELS added to the existing Galaxy Zoo releases, your combined classifications of over a million galaxies near and far are now public. We’ve already done some science together with these classifications, but there’s so much more to do. Thanks again for enabling us to learn about the Universe. This wouldn’t have been possible without you.

Galaxy Zoo: Hubble – data release and paper accepted!

I’m incredibly happy to report that the main paper for the Galaxy Zoo: Hubble project has just been accepted to the Monthly Notices of the Royal Astronomical Society! It’s been a long road for the project, but we’ve finally reached a major milestone. It’s due to the efforts of many, including the scientists who designed the interface and processed the initial images, the web developers who managed our technology and databases, more than 80,000 volunteers who spent time classifying galaxies and discussing them on the message boards, and the distributed GZ science team who have been steadily working on analyzing images, calibrating data, and writing the paper.

The preprint for the Galaxy Zoo: Hubble paper is available here. The release of GZH also syncs up with the publication of the Galaxy Zoo: CANDELS catalog, led by Brooke Simmons; she’ll have a blog post up later today, and the GZC paper is also available as a preprint.

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The first page of the project description and data release paper for Galaxy Zoo: Hubble (Willett et al. 2016).

Galaxy Zoo: Hubble began in 2010; it was the first work of GZ to move beyond the images taken with the Sloan Digital Sky Survey (SDSS). We were motivated by the need to study the evolution and formation of galaxies billions of years ago, in the early days of the Universe. While SDSS is an amazing telescope, it doesn’t have the sensitivity or resolution to make a quality image of a typical galaxy beyond a redshift of about z=0.4 (distances of a few billion parsecs). Instead, we used images from the Hubble Space Telescope, the flagship and workhorse telescope of NASA for the past two decades, and asked volunteers to help us classify the shapes of galaxies in several of Hubble’s largest and deepest surveys. After more than two years of work, the initial set of GZH classifications were finished in 2012 and the site moved on to other datasets, including CANDELS, UKIDSS, and Illustris.

So why has it taken several years to finish the analysis and publication of the data? The reduction of the GZH data ended up being more complicated and difficult than we’d originally anticipated. One key difference lies in our approach to a technique we call debiasing; these refer to sets of corrections made to the raw data supplied by the volunteers. There’s a known effect where galaxies that are less bright and/or further away will appear dimmer and/or smaller in the images which are being classified. This skews the data, making it appear that there are more elliptical/smooth galaxies than truly exist in the Universe. With SDSS images, we dealt with this by assuming that the nearest galaxies were reliably measured, and then deriving corrections which we applied to the rest of the sample.

In Galaxy Zoo: Hubble, we didn’t have that option available. The problem is that there are two separate effects in the data that affect morphological classification. The first is the debiasing issue just mentioned above; however, there’s also a genuine change in the populations of galaxies between, say, 6 billion years ago and the present day. Galaxies in the earlier epochs of the Universe were more likely to have clumpy substructures and less likely to have very well-settled spiral disks with features like bars. So if we just tried to correct for the debiasing effect based on local galaxies, we would have explicitly removed any of the real changes in the population over cosmic time. Since those trends are exactly what we want to study, we needed another approach.

Our solution ended up bringing in another set of data to serve as the calibration. Volunteers who have classified on the current version of the site may remember classifying the “FERENGI” sample. These were images of real galaxies that we processed with computer codes to make them look like they were at a variety of distances. The classifications for these images, which were completed in late 2013, gave us the solution to the first effect; we were able to model the relationship between distance to the galaxy and the likelihood of detecting features, and then applied a correction based on that relationship to the real GZH data.

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Top: Example of a galaxy image processed with FERENGI to make it appear at a variety of distances. Bottom: Calibration curves based on FERENGI data that measure the effect of distance on morphological classification. From Willett et al. (2016).

The new GZH data is similar in format and structure to the data release from GZ2. The main product is a very large data table (113,705 rows by 172 columns) that researchers can slice and dice to study specific groups of galaxies with morphological measurements. We’re also releasing data from several related image sets, including experiments on fading and swapping colors in images, the effect of bright active galactic nuclei (AGN), different exposure depths, and even a low-redshift set of SDSS Stripe 82 galaxies classified with the new decision tree. All of the data will be published in electronic tables along with the paper, and are also downloadable from data.galaxyzoo.org. Our reduction and analysis code is available as a public Github repository.

The science team has already published two papers based on preliminary Galaxy Zoo: Hubble data. This included a paper led by Edmond Cheung (UCSC/Kavli IPMU) that concluded that there is no evidence connecting galactic bars and AGN over a range of redshifts out to z = 1.0. Tom Melvin (U. Portsmouth) carefully examined the overall bar fraction in disks using COSMOS data, measuring a strong decrease in bar fraction going back to galaxies 7.8 billion years ago. We’re now excited to continue new research areas, including a project led by Melanie Galloway (U. Minnesota) on the evolution of red disk galaxies over cosmic time. We hope GZH will enable a lot more science very soon from both our team and external researchers, now that the data are publicly released.

A massive “thank you” again to everyone who’s helped with this project. Galaxy Zoo has made some amazing discoveries with your help in the past eight years, and now that two new unique sets of data are openly available, we’re looking forward to many more.

Bayesian View of Galaxy Evolution

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The Universe is pretty huge, and to understand it we need to collect vast amounts of data. The Hubble Telescope is just one of many telescopes collecting data from the Universe. Hubble alone produces 17.5 GB of raw science data each week. That means since its launch to low earth orbit in April 1990, it’s collected roughly a block of data equivalent in size to 6 million mp3 songs! With the launch of NASA’s James Webb Telescope  just around the corner – (a tennis court sized space telescope!), the amount of raw data we can collect from the Universe is going to escalate dramatically. In order to decipher what this data is telling us about the Universe we need to use sophisticated statistical techniques. In this post I want to talk a bit about a particular technique I’ve been using called a Markov-Chain-Monte-Carlo (MCMC) simulation to learn about galaxy evolution.

Before we dive in into the statistics let me try and explain what I’m trying to figure out. We can model galaxy evolution by looking at a galaxy’s star formation rate (SFR) over time. Basically we want know to how fast a particular galaxy is making stars at any given time. Typically, a galaxy has an initial constant high SFR then at a time called t quench (tq) it’s SFR decreases exponentially which is characterised by a number called tau. Small tau means the galaxy stops forming stars, or is quenched, more rapidly. So overall for each galaxy we need to determine two numbers tq and tau to figure out how it evolved. Figure 1 shows what this model looks like.

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Figure 1: Model of a single galaxy’s SFR over time. Showing an initial high constant SFR, follow by a exponential quench at tq.

To calculate these two numbers, tq and tau, we look at the colour of the galaxy, specifically the UVJ colour I mentioned in my last post. We then compare this to a predicted colour of a galaxy for a specific value of tq and tau. The problem is that there are many different combinations of tq and tau, how to we find the best match for a galaxy? We use a MCMC simulation to do this.

The first MC – Markov-Chain – just means an efficient random walk. We send “walkers” to have a look around for a good tq and tau, but the direction we send them to walk at each step depends on how good the tq and tau they are currently at is. The upshot of this is we quickly home in on a good value of tq and tau. The second MC – Monte Carlo – just picks out random values of tq and tau and tests how good they are by comparing the UVJ colours and our SFR model. Figure 2 shows a gif of a MCMC simulation of a single galaxy. The histograms shows the positions of the walkers searching the tq and tau space, and the blue crosshair shows the best fit value of tq and tau at every step. You can see the walkers homing in and settling down on the best value of tq and tau. I ran this simulation by running a modified version of the starpy code.

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Figure 2: MCMC simulation for a single galaxy, pictured in the top right corner. Main plot shows density of walkers. Marginal histograms show 1D projections of walker densities. Blue crosshair shows best fit values of tq and tau at each step.

The maths that underpins this simulation is called Bayesian Statistics, and it’s quite a novel way of thinking about parameters and data. The main difference is that instead of treating unknown parameters as fixed quantities with associated error, they are treated as random variables described by probability distributions. It’s quite a powerful way of looking at the Universe! I’ve left all of the gory maths detail about MCMC out but if you’re interested an article by a DPhil student here at Oxford does are really good job of explaining it here.

So how does this all relate to galaxy morphology, and Galaxy Zoo classifications? I’m currently running the MCMC simulation showing in Figure 2 over the all the galaxies in the COSMOS survey. This is really cool because apart from getting to play with the University of Oxford’s super computer (544 cores!), I can use galaxy zoo morphology to see if the SFR of a galaxy over time is dependent on the galaxy’s shape, and overall learn what the vast amount of data I have says about galaxy evolution.

@petermcgill94

New paper on active black holes affecting star formation rates!

Good news everyone, another Galaxy Zoo paper was published today! This work was led by yours truly (Hi!) and looks at the impact that the central active black holes (active galactic nuclei; AGN) can have on the shape and star formation of their galaxy. It’s available here on astro-ph: http://arxiv.org/abs/1609.00023 and will soon be published in MNRAS.

Turns out, despite the fact that these supermassive black holes are TINY in comparison to their galaxy (300 light years across as opposed to 100,000 light years!) we see that within a population of these AGN galaxies the star formation rates have been recently and rapidly decreased. In a control sample of galaxies that don’t currently have an AGN in their centre, we don’t see the same thing happening. This phenomenon has been seen before in individual galaxies and predicted by simulations but this is the first time its been statistically shown to be happening within a large population. It’s tempting to say then that it’s the AGN that is directly causing this drop in the star formation rate (maybe because the energy thrown out by the active black hole blasts out or heats the gas needed to fuel star formation) but with the data we have we can’t say for definite if the AGN are the cause. It could be that this drop in star formation is being caused by another means entirely, which also coincidentally turns on an AGN in a galaxy.

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A random sample of galaxies which host a central active black hole used in this work. The disc vote fraction classification from Galaxy Zoo 2 is shown for each image. Images from SDSS.

These galaxies were also all classified by our wonderful volunteers in Galaxy Zoo 2 which meant that we could also look whether this drop in the star formation rate was dependent on the morphology of the galaxy; turns out not so much! If the drop in the star formation rate is being caused directly by the AGN (and remember we still can’t say for sure!) then the central black hole of a galaxy doesn’t care what shape galaxy it’s in. An AGN will affect all galaxies, regardless of morphology, just the same.

 

 

9 Years of Galaxy Zoo

Last year we had so much fun celebrating all that we (including you) had accomplished over the first 8 years of Galaxy Zoo. This year, for our 9th birthday, we thought we’d hand things directly over to you. We sent out a newsletter asking people about their favorite Galaxy Zoo science. We asked people to rank five choices:

  • Hanny’s Voorwerp & the Voorwerpjes (ionized clouds and active galaxies)
  • Green Peas (highly compact & star-forming galaxies)
  • Red spirals (disk galaxies with no/little star formation)
  • Blue ellipticals (spheroid galaxies with ongoing/retriggered star formation)
  • Bars (the galaxy kind; how this mode of disk galaxies drives galaxy evolution)

We’ve now collected just over 200 responses and combined your rankings. Although the distributions were pretty similar, and all the options had plenty of people choosing it as their favorite, one of the options jumped out as a pretty clear leader (at least in this rather informal poll).

Bars - the galaxy kind!

Bars – the galaxy kind!

Of course, the list we asked people to choose from is by no means complete, especially if you include not just the main Galaxy Zoo but also its related projects. In the “Other” box we had a variety of entries, with some mentioning galaxies found in Radio Galaxy Zoo and others citing those seen in Galaxy Zoo: Bar Lengths. Plenty of people mentioned galaxy mergers, and gravitational lenses got a few mentions too! If we had a complete list the rankings would likely be different, but then again, that would be such a long list I’d be worried many fewer people would want to answer.

We also had a space for people to enter whatever text they wanted at the end of the survey, and the responses were varied, interesting, and a treat to read. Here’s a sample (each paragraph is a separate comment):

I do not spend a lot of time here, but when I have the time, I love it. Thank you!

What a great way to feel like a scientist.

I’ve been an on-and-off participant in the Zooniverse citizen science projects since I was 13 years old – and Galaxy Zoo has been one of my favourites for a while! I just wanted to say thank you for providing the opportunity for an ordinary teenager to feel included in fascinating scientific research – that experience has inspired me to pursue a degree in Physics and Astronomy in the fall.

Go Science!

We were also curious about who, as a group, we were asking these questions of. It turns out that quite a large fraction of people who responded to the survey have been with us since the early days, which is so lovely. And we were also delighted to see people engaging with us who’ve just recently discovered Galaxy Zoo. We are so glad all of you are collaborating with us; here’s to many years to come.

Thank you!

P.S. – The big 10 is coming next year… what would you like to see for the occasion?

Galaxy Zoo and the COSMOS Survey.

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Hello present, and hopefully future volunteers!

I’m a summer research intern on the Zooniverse Project, based at the University of Oxford. I’m currently at university in London and I’ll be going into my fourth year of studying Theoretical Physics. I’m three weeks into my internship, and I want to share with you how the hundreds-of-thousands of galaxies you’ve worked hard to classify are being used in research.

I’m working with Galaxy Zoo Hubble (GZH) data, which are classifications of galaxies from the Hubble Space Telescope Legacy survey. The classifications for this data have just been submitted for publication by a group of researchers from Galaxy Zoo, and you can read about it here. Specifically I’m working with a subset of this data from the Cosmic Evolution Survey, or COSMOS. This survey is specially designed to help us understand how galaxies evolve over time, and how their local environments in the universe affect this.

Up to now I’ve been using GZH data to add morphology to data currently found in the literature, in the hope that we can learn something new about galaxy evolution. In this post I want to share with you a particular striking example of how GZH classifications have transformed current data. Figure 1 shows two rows of colour-colour plots. The vertical axis is U-V colour, which is a measure how much recent star formation is going on in a galaxy – the higher up a galaxy is in the plot the more recent star formation is going on. The horizontal axis is V-J colour which is a measure of how much Infrared light compared to visible light a galaxy is emitting – the further left a galaxy is in the plot the generally older and more ‘dead’ it is. The first row (top) is found in a paper (Muzzin et al 2013), on analysis of galaxies in the COSMOS survey, written by researchers from the US, Denmark, Netherlands, UK, and Chile. The second row (bottom) shows the same data but with GZH classifications overlaid. Red and blue points represent featured and smooth galaxies respectively. Banner image shows a featured spiral galaxy (left), and and smooth elliptical galaxy (right).

Figure1

Figure 1: colour-colour plots Galaxies from the COSMOS survey (top) before (bottom) after GZH classifications data added. Red and blue points represent featured and smooth galaxies respectively.

No need to ask which one looks more interesting! Lets understand what these plots mean. Each point on each plot represents a different galaxy. On each row the plots are sorted by z or redshift; you can think of this as being different snapshots of galaxies in the universe at different times. The most recent snapshot being on the left, and the oldest on the right of each row.

The important thing to take away from this data is that there are two distinct blobs or populations of galaxies in each plot. Galaxies in the top left blob are called star forming (SF) and galaxies in the longer bottom right blob are non-star forming, or ‘quiescent’. From the overlay of GZH classifications data on Figure 1 (bottom), we can see that the nearly complete absence of galaxies with features in the top left population of SF galaxies – something that we didn’t know before!

So why do we care about analysing colour-colour plots of galaxies? As a galaxy evolves through its lifetime it moves from the SF population to the quiescent through that bit in-between the two blobs, which is called the ‘Green Valley’ (I’ll save more on that for another blog post), and the truth is nobody quite knows how this happens. Overall, we hope GZH classifications may shed some light on this, and help us understand how galaxies evolve.

To help us finally understand the evolution of galaxies, get involved right now at www.galaxyzoo.org, we’d be happy to have you on-board!

@petermcgill94