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.
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.
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.
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.
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.
Since our discovery in 2010 that the red spirals identified by your classifications in the first phase of Galaxy Zoo were twice as likely to host galactic scale bars as normal blue spirals, a lot of our research time has focused on understanding which types of galaxies host bars, and why that might be.
Our research with the bars identified by you in the second phase of Galaxy Zoo continues to gives us hints that these structures in galaxies might be involved in the process which quenches star formation in spiral galaxies and through that could be part of the process involved in the reduction of star formation in the universe as a whole.
We’ve also used your classifications as part of Galaxy Zoo Hubble and Galaxy Zoo CANDELS to identify the epoch in the universe when disc galaxies were first stable enough to host a significant number of bars, finding them possibly even earlier in the Universe than was previously thought.
Last Friday I spoke at the monthly “Ordinary Meeting” of the Royal Astronomical Society, giving summary of the evidence we’re collecting on the impact bars have on galaxies thanks to your classifications (a video of my talk will be available at some point). This was the second time I’ve spoken at this meeting about results from Galaxy Zoo, and it’s a delightful mix of professional colleagues, and enthusiastic amateurs – including some Galaxy Zoo volunteers.
Prompted by that I thought it was timely to write on this blog about what these bars really are, what they do to galaxies, and why I think they’re so interesting. I wrote the below some time ago when I had a spare few minutes, and was just looking for the right time to post it.
The thing about galaxies, which is sometimes hard to remember, is that they are simply vast collections of stars, and that those stars are all constantly in motion, orbiting their common centre of mass. The structures that we see in galaxies are just a snapshot of the locations of those stars right now (on a cosmic timescale), and the patterns we see in the positions of the stars reveals patterns in their orbital motions. A stellar bar for example reveals a set of very elongated orbits of stars in the disc of a galaxy.
Another extraordinary thing about a disc galaxy is how thin it is. To put this is perspective I’ll give you a real world example. In the Haus der Astronomie in Heidelberg you can walk around inside a scale model of the Whirlpool galaxy. The whole building was laid out in a design which reflects the spiral arms of this galaxy. However it’s not an exact scale model – to properly represent the thickness of the disc of the Whirlpool galaxy the building (which in actual fact has 3 stories and hosts a fairly large planetarium in its centre) would have to be only 90cm tall…..
Such an incredibly thin disc of stars floating independently in space would be quite unstable dynamically (meaning its own gravity should cause it to buckle and collapse on itself). This instability would immediately manifest in elongated orbits of stars, which would make a stellar bar (as part of this process of collapse). Simple computer models of disks of stars immediately form bars. Of course we now know that galaxy discs are submerged in massive halos of dark matter. So my first favourite little fact about bars is
(1) the fact that not all disc galaxies have bars was put forward as evidence that the discs must be embedded in massive halos before the existence of dark matter was widely accepted.
Now we can model dark matter halos better we discover that even with a dark matter halo, as long as that halo can absorb angular momentum (ie. rotate a bit) all discs will eventually make a bar. So my second favourite little fact is that
(2) we still don’t understand why not all disc galaxies have bars.
What this second fact means is that perhaps what I should really be doing is studying the galaxies you have identified as not having bars to figure out why it is they haven’t been able to form a bar yet. It should really be the properties of these which are unexpected….. We find that this is more likely to happen in blue, intermediate mass spirals with a significant reservoir of atomic hydrogen (the raw material for future star formation). In fact this last thing may be the most significant. Including realistic interstellar gas in computer simulation of galaxies is very difficult, but people do run what is called “smooth particle hydrodynamic” simulations (basically making “particles” of gas and inserting the appropriate properties). If they add too much gas into these simulations they find that bar formation is either very delayed, or doesn’t happen in the time of the simulation…..
Anyway I hope this has given you a flavour of what I find interesting about bars in galaxies. I think it’s fascinating that they give us a morphological way to identify a process which is so dynamical in nature. And it’s a very complex process, even though the basic physics (just orbits of stars) is very simple and well understood. Finally, I have become convinced though tests of the bars identified by you in Galaxy Zoo compared to bars identified by other methods, that if you want a clean sample of very large bars in galaxies that multiple independent human eyes will give you the best result. You are much less easy to trick that automated methods for finding galactic bars.
So thanks again for the classifications, and keep clicking. 🙂
I’ve used some statistical tools to analyze the spatial distribution of Galaxy Zoo galaxies and to see whether we find galaxies with particular classifications in more dense environments or less dense ones. By “environment” I’m referring to the kinds of regions that these galaxies tend to be found: for example, galaxies in dense environments are usually strongly clustered in groups and clusters of many galaxies. In particular, I’ve used what we call “marked correlation functions,” which I’ve found are very sensitive statistics for identifying and quantifying trends between objects and their environments. This is also important from the perspective of models, since we think that massive clumps of dark matter are in the same regions as massive galaxy groups.
We’ve mainly used them in two papers, where we analyzed the environmental dependence of morphology and color and where we analyzed the environmental dependence of barred galaxies. These papers have been described a bit in this post andthis post. We’ve also had other Galaxy Zoo papers about similar subjects, especially this paper by Steven Bamford and this one by Kevin Casteels.
What I loved about these projects is that we obtained impressive results that nobody else had seen before, and it’s all thanks to the many many classifications that the citizen scientists have contributed. These statistics are useful only when one has large catalogs, and that’s exactly what we had in Galaxy Zoo 1 and 2. We have catalogs with visual classifications and type likelihoods that are ten times as large as ones other astronomers have used.
What are these “marked correlation functions”, you ask? Traditional correlation functions tell us about how objects are clustered relative to random clustering, and we usually write this as 1+ ξ. But we have lots of information about these galaxies, more than just their spatial positions. So we can weight the galaxies by a particular property, such as the elliptical galaxy likelihood, and then measure the clustering signal. We usually write this as 1+W. Then the ratio of (1+W)/(1+ξ), which is the marked correlation function M(r), tells us whether galaxies with high values of the weight are more dense or less dense environments on average. And if 1+W=1+ξ, or in other words M=1, then the weight is not correlated with the environment at all.
First, I’ll show you one of our main results from that paper using Galaxy Zoo 1 data. The upper panel shows the clustering of galaxies in the sample we selected, and it’s a function of projected galaxy separation (rp). This is something other people have measured before, and we already knew that galaxies are clustered more than random clustering. But then we weighted the galaxies by the GZ elliptical likelihood (based on the fraction of classifiers identifying the galaxies as ellipticals) and then took the (1+W)/(1+ξ) ratio, which is M(rp), and that’s shown by the red squares in the lower panel. When we use the spiral likelihoods, the blue squares are the result. This means that elliptical galaxies tend to be found in dense environments, since they have a M(rp) ratio that’s greater than 1, and spiral galaxies are in less dense environments than average. When I first ran these measurements, I expected kind of noisy results, but the measurements are very precise and they far exceeded my expectations. Without many visual classifications of every galaxy, this wouldn’t be possible.
Second, using Galaxy Zoo 2 data, we measured the clustering of disc galaxies, and that’s shown in the upper panel of the plot above. Then we weighted the galaxies by their bar likelihoods (based on the fractions of people who classified them as having a stellar bar) and measured the same statistic as before. The result is shown in the lower panel, and it shows that barred disc galaxies tend to be found in denser environments than average disc galaxies! This is a completely new result and had never been seen before. Astronomers had not detected this signal before mainly because their samples were too small, but we were able to do better with the classifications provided by Zooites. We argued that barred galaxies often reside in galaxy groups and that a minor merger or interaction with a neighboring galaxy can trigger disc instabilities that produce bars.
What kinds of science shall we use these great datasets and statistics for next? My next priority with Galaxy Zoo is to develop dark matter halo models of the environmental dependence of galaxy morphology. Our measurements are definitely good enough to tell us how spiral and elliptical morphologies are related to the masses of the dark matter haloes that host the galaxies, and these relations would be an excellent and new way to test models and simulations of galaxy formation. And I’m sure there are many other exciting things we can do too.
Galaxy Zoo started in 2007 because astronomers had 1,000,000 galaxies that needed to be sorted, classified, and examined. After the incredible response from the public, the zookeepers realized that this kind of problem wasn’t limited to galaxies, nor even just to astronomy, and the Zooniverse was born.
Now, seven actual years, close to 30 projects, more than 60 publications, and hundreds of years’ worth of human effort later, the Zooniverse has just registered its 1,000,000th volunteer. Given that Galaxy Zoo was the project that led to the creation of the Zooniverse, it seems fitting that its millionth citizen scientist joined to classify galaxies! That volunteer (whose identity we won’t divulge unless s/he gives us permission) joins over 400,000 others who have classified galaxies near and far. That number is 40% of the Zooniverse’s overall total — meaning that, while Galaxy Zoo has a large and vibrant community of volunteers and scientists, most people who join Zooniverse start off contributing to a different project. Many of them try other projects after their first: over on the Zooniverse blog Rob described the additions we’ve made to the Zooniverse Home area so that everyone who brought us to a million can see their own contribution “fingerprint” on the Zooniverse. Here’s what mine currently looks like:
Our millionth volunteer gets a cheesy prize (but hopefully useful: a Zooniverse tote bag and mug), and while we’d like to give that same prize to the 999,999 who came before him/her and to everyone who contributes to Galaxy Zoo and all Zooniverse projects, perhaps it’s more fitting that we say to everyone what’s really on our mind right now:
Great news everybody! The latest Galaxy Zoo 1 paper has been accepted by MNRAS and has appeared on astro-ph: http://arxiv.org/abs/1402.4814
In this paper, we take a look at the most crucial event in the life of a galaxy: the end of star formation. We often call this process “quenching” and many astrophysicists have slightly different definitions of quenching. Galaxies are the place where cosmic gas condenses and, if it gets cold and dense enough, turns into stars. The resulting stars are what we really see as traditional optical astronomers.
Not all stars shine the same way though: stars much more massive than our sun are very bright and shine in a blue light as they are very hot. They’re also very short-lived. Lower mass stars take a more leisurely pace and don’t shine as bright (they’re not as hot). This is why star-forming galaxies are blue, and quiescent galaxies (or “quenched” galaxies) are red: once star formation stops, the bluest stars die first and aren’t replaced with new ones, so they leave behind only the longer-lived red stars for us to observe as the galaxy passively evolves.
Blue Ellipticals & Red Spirals
The received wisdom in galaxy evolution had been that spirals are blue, and ellipticals are red, meaning that spirals form new stars (or rather: convert gas into stars) and ellipticals do not form new stars (they have no gas to convert to stars). Since you’re taking part in Galaxy Zoo, you know that this isn’t entirely true: there are blue (star-forming) ellipticals and red (passive) spirals. It’s those unusual objects that we started Galaxy Zoo for, and in this paper they help us piece together how, why and when galaxies shut down their star formation. You can already conclude from the fact that blue ellipticals and red spirals exist that there is no one-to-one correlation between a galaxy’s morphology and whether or not it’s forming stars.
Blue, Red and…. Green?
A few years back, astronomers noticed that not all galaxies are either blue and star forming or red and dead. There was a smaller population of galaxies in between those two, which they termed the “green valley” (the origin of the term is rather interesting and we talk about it in this Google+ hangout). So how do these “green” galaxies fit in? The natural conclusion was that these “in between” galaxies are the ones who are in the process of shutting down their star formation. They’re the galaxies which are in the process of quenching. Their star formation rate is dropping, which is why they have fewer and fewer young blue stars. With time, star formation should cease entirely and galaxies would become red and dead.
The Green Valley is a Red Herring
Ok, why is this green valley a red herring you ask? Simple: the green valley galaxies aren’t a single population of similar galaxies, but rather two completely different populations doing completely different things! And what’s the biggest evidence that this is the case? Some of them are “green spirals” and others are “green ellipticals”! (Ok, you probably saw that coming from a mile away).
So, we have both green spirals and green ellipticals. First: how do we know they must be doing very different things? If you look at the colour-mass diagram of only spirals and only ellipticals, we start to get some hints. Most ellipticals are red. A small number are blue, and a small number are green. If the blue ellipticals turn green and then red, they must do so quickly, or there would be far more green ellipticals. There would be a traffic jam in the green valley. So we suspect that quenching – the end of star formation – in ellipticals happens quickly.
In the case of spirals, we see lots of blue ones, quite a few green one and then red ones (Karen Masters has written several important Galaxy Zoo papers about these red spirals). If spirals slowly turn red, you’d expect them to start bunching up in the middle: the green “valley” which is revealed to be no such thing amongst spirals.
Galaxy Quenching time scales
We can confirm this difference in quenching time scales by looking at the ultraviolet and optical colours of spirals and ellipticals in the green valley. What we see is that spirals start becoming redder in optical colours as their star formation rate goes down, but they are still blue in the ultraviolet. Why? Because they are still forming at least some baby stars and they are extremely bright and so blue that they emit a LOT of ultraviolet light. So even as the overall population of young stars declines, the galaxy is still blue in the UV.
Ellipticals, on the other hand, are much redder in the UV. This is because their star formation rate isn’t dropping slowly over time like the spirals, but rather goes to zero in a very short time. So, as the stellar populations age and become redder, NO new baby stars are added and the UV colour goes red.
It’s all about gas
Galaxies form stars because they have gas. This gas comes in from their cosmological surroundings, cools down into a disk and then turns into stars. Galaxies thus have a cosmological supply and a reservoir of gas (the disk). We also know observationally that gas turns into stars according to a specific recipe, the Schmidt-Kennicutt law. Basically that law says that in any dynamical time (the characteristic time scale of the gas disk), a small fraction (around 2%) of that gas turns into stars. Star formation is a rather inefficient process. With this in mind, we can explain the behaviour of ellipticals and spirals in terms of what happens to their gas.
Spirals are like Zombies
Spirals quench their star formation slowly over maybe a billion years or more. This can be explained by simply shutting off the cosmological supply of gas. The spiral is still left with its gas reservoir in the disk to form stars with. As time goes on, more and more of the gas is used up, and the star formation rate drops. Eventually, almost no gas is left and the originally blue spiral bursting with blue young stars has fewer and fewer young stars and so turns green and eventually red. That means spirals are a bit like zombies. Something shuts off their supply of gas. They’re already dead. But they have their gas reservoir, so they keep moving, moving not knowing that they’re already doomed.
Ellipticals life fast, die young
The ellipticals on the other hand quench their star formation really fast. That means it’s not enough to just shut off the gas supply, you also have to remove the gas reservoir in the galaxy. How do you do that? We’re not really sure, but it’s suspicious that most blue ellipticals look like they recently experienced a major galaxy merger. There are also hints that their black holes are feeding, so it’s possible an energetic outburst from their central black holes heated and ejected their gas reservoir in a short episode. But we don’t know for sure…
So that’s the general summary for the paper. Got questions? Ping me on twitter at @kevinschawinski
The guest post below is by Zach Pace, an undergraduate physics student at the University of Buffalo. Zach worked at the University of Minnesota during the summer of 2013 through the NSF’s Research Experience for Undergraduates (REU) program. Zach is continuing to work with Galaxy Zoo data as part of his senior thesis.
My name is Zach Pace. I’m an undergraduate physics student from the University at Buffalo, and I’ve been working on the Galaxy Zoo 2 project at the University of Minnesota since late May with Kyle Willett and Lucy Fortson. My investigation has been twofold: I have been diagramming specific morphological categories in color-magnitude space, and also fitting those data to mathematical functions.
As many readers probably know, a galaxy’s magnitude (overall brightness in the red band, on a log scale) and a galaxy’s color (the difference between the blue magnitude and a red band) are two important quantities for determining what a galaxy might look like (and how it might evolve). Brighter galaxies have more mass (more stars produce more light, of course), and bluer galaxies have a more recent star formation history (this is because young, bright stars tend to be large, bright, and blue). In terms of the whole population, we know, for instance, that elliptical galaxies tend to concentrate in a red sequence, and have typical colors between 2.25 and 2.75. Conversely, the vast majority of spiral galaxies concentrate in a blue cloud between colors 1.25 and 2.0. These two populations are clearly separated in color-magnitude space (this can be seen in the accompanying 2-D histogram, made from Zoo 2 data).
One of the main goals of Zoo 2 is to gauge the extent to which morphology informs physical characteristics like color and magnitude, so my objective for the summer was to come up with good representations of color and magnitude for all of the smaller sub-populations in Zoo 2.
Several of my results were interesting and surprising. For instance, it has been suggested that spiral galaxies with more arms and spiral galaxies with tighter arm winding (which is to say, a shallower pitch angle) tend to be brighter and bluer. This can be intuitively understood as follows: tighter winding of spiral arms and the presence of more spiral arms indicate, on average, denser gas clouds in those arms, which is tied to increased star formation and bluer color. However, I wasn’t able to measure this in the Zoo 2 data (all the differences were on the order of the histograms’ bin size, about 0.1 magnitude, or about a 10% difference in brightness). This suggests that spiral galaxies, no matter arm multiplicity or winding, are drawn from the same base population.
I also came across something unexpected when looking at bulge sizes in face-on disk galaxies. The distribution of galaxies classified by users as bulgeless is starkly different from the distribution of obvious bulge and bulge-dominated galaxies. Furthermore, the population with a bulge that is just noticeable seems to form an intermediate population between the bulgeless and bulge. This observation is also borne out in edge-on disk galaxies: the population of bulgeless edge-on galaxies has a similar shape to the population of face-on galaxies, albeit with stronger reddening on the bright end.
To fit the distributions, I used a method pioneered about 10 years ago by Ivan Baldry, which fits one parameter after another in our profile functions to find a distribution that converges onto the best fit. It works okay (but not great) for the whole sample, and it fails pretty badly when working with the smaller sub-populations. This is because I have to fit many parameters at once, and do that a bunch of times in a row for the fit to converge, so there are a lot of points of failure. I’m working now at Buffalo towards finding a different and better fitting routine, which will allow us to represent more distributions mathematically.
If you have any questions, feel free to comment below.
Over the years the public has seen more than a million galaxies via Galaxy Zoo, and nearly all of them had something in common: we tried to get as close as possible to showing you what the galaxy would actually look like with the naked eye if you were able to see them with the resolving power of some of the world’s most advanced telescopes. Starting today, we’re branching out from that with the addition of over 70,000 new galaxy images (of some our old favorites) at wavelengths the human eye wouldn’t be able to see.
Just to be clear, we haven’t always shown images taken at optical wavelengths. Galaxies from the CANDELS survey, for example, are imaged at near-infrared* wavelengths. But they are also some of the most distant galaxies we’ve ever seen, and because of the expansion of the universe, most of the light that the Hubble Space Telescope (HST) captured for those galaxies had been “stretched” from its original optical wavelength (note: we call the originally emitted wavelength the rest-frame wavelength).
Optical light provides a huge amount of information about a galaxy (or a voorwerpje, etc.), and we are still a long way from having extracted every bit of information from optical images of galaxies. However, the optical is only a small part of the electromagnetic spectrum, and the other wavelengths give different and often complementary information about the physical processes taking place in galaxies. For example, more energetic light in the ultraviolet tells us about higher-energy phenomena, like emission directly from the accretion disk around a supermassive black hole, or light from very massive, very young stars. As a stellar population ages and the massive stars die, the older, redder stars left behind emit more light in the near-infrared – so by observing in the near-IR, we get to see where the old stars are.
The near-IR has another very useful property: the longer wavelengths can mostly pass right by interstellar dust without being absorbed or scattered. So images of galaxies in the rest-frame infrared can see through all but the thickest dust shrouds, and we can get a more complete picture about stars and dust in galaxies by looking at them in the near-IR.
Starting today, we are adding images of galaxies taken with the United Kingdom Infrared Telescope (UKIRT) for the recently-completed UKIDSS project. UKIDSS is the largest, deepest survey of the sky at near-infrared wavelengths, and the typical seeing is close to (often better than) the typical seeing of the SDSS. Every UKIDSS galaxy that we’re showing is also in SDSS, which means that volunteers at Galaxy Zoo will be providing classifications for the same galaxies in both optical and infrared wavelengths, in a uniform way. This is incredibly valuable: each of those wavelength ranges are separately rich with information, and by combining them we can learn even more about how the stars in each galaxy have evolved and are evolving, and how the material from which new stars might form (as traced by the dust) is distributed in the galaxy.
In addition to the more than 70,000 UKIDSS near-infrared images we have added to the active classification pool, we are also adding nearly 7,000 images that have a different purpose: to help us understand how a galaxy’s classification evolves as the galaxy gets farther and farther away from the telescope. To that end, team member Edmond Cheung has taken SDSS images of nearby galaxies that volunteers have already classified, “placed” them at much higher redshifts, then “observed” them as we would have seen them with HST in the rest-frame optical. By classifying these redshifted galaxies**, we hope to answer the question of how the classifications of distant galaxies might be subtly different due to image depth and distance effects. It’s a small number of galaxies compared to the full sample of those in either Galaxy Zoo: Hubble or CANDELS, but it’s an absolutely crucial part of making the most of all of your classifications.
As always, Galaxy Zoo continues to evolve as we use your classifications to answer fundamental questions of galaxy evolution and those answers lead to new and interesting questions. We really hope you enjoy these new images, and we expect that there will soon be some interesting new discussions on Talk (where there will, as usual, be more information available about each galaxy), and very possibly new discoveries to be made.
Thanks for classifying!
* “Infrared” is a really large wavelength range, much larger than optical, so scientists modify the term to describe what part of it they’re referring to. Near-infrared means the wavelengths are only a bit too long (red) to be seen by the human eye; there’s also mid-infrared and far-infrared, which are progressively longer-wavelength. For context, far-infrared wavelengths can be more than a hundred times longer than near-infrared wavelengths, and they’re closer in energy to microwaves and radio waves than optical light. Each of the different parts of the infrared gives us information on different types of physics.
** You might notice that these galaxies have a slightly different question tree than the rest of the galaxies: that’s because, where these galaxies have been redshifted into the range where they would have been observed in the Galaxy Zoo: Hubble sample, we’re asking the same questions we asked for that sample, so there are some slight differences.
Top Image Credits and more information: here.
This week much of the team has been in Sydney, Australia, for the Evolutionary Paths In Galaxy Morphology conference. It’s a meeting centered largely around Galaxy Zoo, but it’s more generally about galaxy evolution, and how Galaxy Zoo fits into our overall (ever unfolding) picture of galaxy evolution.
The first talk of the conference was a public talk by Chris, fitting for a project that would not have been possible without public participation. Chris also gave a science talk later in the conference, summarizing many of the different results from Galaxy Zoo (and with a focus on presenting the results of team members who couldn’t be at the meeting). For me, Karen’s talk describing secular galaxy evolution and detailing the various recent results that have led us to believe “slow” evolution is very important was a highlight of Tuesday, and the audience questions seemed to express a wish that she could have gone on for longer to tie even more of it together. When the scientists at a conference want you to keep going after your 30 minutes are up, you know you’ve given a good talk.
In fact, all of the talks from team members were very well received, and over the course of the week so far we’ve seen how our results compare to and complement those of others, some using Galaxy Zoo data, some not. We’ve had a number of interesting talks describing the sometimes surprising ways the motions of stars and gas in galaxies compare with the visual morphologies. Where (and how bright) the stars and dust are in a galaxy doesn’t always give clues to the shape of the stars’ orbits, nor the extent and configuration of the gas that often makes up a large fraction of a galaxy’s mass.
This goes the other way, too: knowing the velocities of stars and gas in a galaxy doesn’t necessarily tell you what kinds of stars they are, how they got there, or what they’re doing right now. I suspect a combination of this kinematic information with the image information (at visual and other wavelengths) will in the future be a more often used and more powerful diagnostic tool for galaxies than either alone.
Overall, the meeting was definitely a success, and throughout the meeting we tried to keep a record of things so that others could keep up with the conference even if they weren’t able to attend. There was a lot of active tweeting about the conference, for example, and Karen and I took turns recording the tweets so that we’d have a record of each day of the Twitter discussion. Here those are, courtesy of Storify:
Also, remember at our last hangout when we said we’d have a hangout from Sydney? That proved a bit difficult, not just because of the packed meeting schedule but also because of bandwidth issues: overburdened conference and hotel wifi connections just aren’t really up to the task of streaming a hangout. We eventually found a place, but then it turned out there was construction going on next door, so instead of the sunny patio we had intended to run the hangout from we ended up in an upstairs bedroom to get as far away from the noise as possible. Ah, well. You can see our detailed discussion of how the meeting went below, including random contributions from the jackhammer next door (but only for the first few minutes):
And now we’ll all return (eventually) to our respective institutions to reflect on the meeting, start work on whatever new ideas the conference discussions, talks and posters started brewing, and continue the work we had set aside for the past week. None of this is really as easy as it sounds; the best meetings are often the most exhausting, so it takes some time to recover. I asked our fearless leader Chris if he had a pithy statement to sum up his feeling of exhilarated post-meeting fatigue, and he took my keyboard and offered the following:
gt ;////cry;gvlbhul,kubmc ;dptfvglyknjuy,pt vgybhjnomk
I’m sure that, if any tears were shed, they were tears of joy. This is a great project and it’s only getting better.