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.
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.
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.
I’m very happy to be posting again to the How-To-Guide. We’ve made a number of updates to Quench data and Quench Tools. Before I launch into Part 3 of the Guide, here are the recent updates:
- The classification results for the 57 control galaxies that needed replacements have been uploaded into Quench Tools.
- We’ve applied two sets of corrections to the galaxies magnitudes: the magnitudes are now corrected for both the effect of extinction by dust and the redshifting of light (specifically, the k-correction).
- We’ve uploaded the emission line characteristics for all the control galaxies.
- We’ve uploaded a few additional properties for all the galaxies (e.g., luminosity distances and star formation rates).
- We corrected a bug in the code that mistakenly skipped galaxies identified as ‘smooth with off-center bright clumps’.
In Part 3 we’re going to use the results from the classifications that you all provided to see if there’s anything different about the post-quenched galaxies that have merged or are in the process of merging with a neighbor, and those that show no merger signatures.
The figure below is of one of my favorite post-quenched galaxies with merger signatures. Gotta love those swooping tidal tails!
Let’s get started!
Step 1: Because of the updates to Tools, first clear your Internet browser’s cache, so it uploads the latest Quench Tools data.
Step 2: Copy my starter dashboard with emission line ratios ready for play.
- Open my Dashboard and click ‘Copy Dashboard’ in the upper right. This way you can make changes to it.
- In this Dashboard, I’ve uploaded the post-quenched galaxy data.
- I also opened a Table, just as you did in Part 2 of this How-To-Guide. I called the Table ‘All Quench Table’.
- In the Table, notice how I’ve applied a few filters, by using the syntax:
filter .’Halpha Flux’ > 0
- This reduces the table to only include sources that fulfill those criteria.
- Also notice that I’ve created a few new columns of data, just as you did in Part 2, by using the syntax:
field ‘o3hb’, .’Oiii Flux’/.’Hbeta Flux’
- That particular syntax means that I took the flux for the doubly ionized oxygen emission line ([0III]) and divided it by the flux in one of the Hydrogen emission lines (Hbeta).
- This ratio and the ratio of [NII]/Halpha are quite useful for identifying Active Galactic Nuclei (AGN).
- It’d be really interesting if we find that AGN play a role in shutting off the star formation in our post-quenched galaxies. A major question in galaxy evolution is whether there’s any clear interplay between merging, AGN activity, and shutting off star formation.
Step 3: Create the BPT diagram using the ratios of [OIII]/Hb and [NII]/Ha.
- BPT stands for Baldwin, Phillips, and Terlevich (1981), among the first articles to use these emission line ratios to identify AGN. Check out the GZ Green Peas project’s use of the BPT diagram.
- Click on ‘Tools’. Choose ‘Scatter plot’ in the pop-up options.
- In the new Scatterplot window, choose ‘All Quench Table’ as your ‘Data Source’.
- For the x-axis, choose ‘logn2ha’. For the y-axis, choose ‘logo3hb’.
- Adjust the min/max values so the data fits nicely within the window, as shown in the figure below.
- Remember that you can click on the comb icon in the upper-left of the plot to make the menu overlay disappear.
- Do you notice the two wings of the seagull in your plot? The left-hand wing is where star forming galaxies reside (potentially star-bursting galaxies) while the right-hand wing is where AGN reside. Our post-quenched sample of galaxies covers both wings.
Step 4: Compare the BPT diagram for post-quenched galaxies with and without signatures of having experienced a merger.
- To do this, you’ll need to first create two new tables, one that filters out merging galaxies and the other that filters out non-merging galaxies.
- Click on ‘Tools’. Choose ‘Table’ in the pop-up options.
- In the new Table window, choose ‘All Quench Table’ as the ‘Data Source’. Notice how this new table already has all the new columns that were created in the ‘All Quench Table’. That makes our life easier!
- Look through the column names and find the one that says ‘Merging’. Possible responses are ‘Neither’, ‘Merging’, ‘Tidal Debris’, or ‘Both’.
- Let’s pick out just the galaxies with no merger signatures.
- Under ‘Prompt’ type:
filter .Merging = ‘Neither’
- If you scroll to the bottom of the Table, you’ll notice that you now have only 2191 rows, rather than the original 3002.
- Call this Table ‘Non-Mergers Table’ by double clicking on the ‘Table-4’ in the upper-left of the Table and typing in the new name.
- Now follow the instructions from Step 3 to create a BPT scatter plot for your post-quenched galaxies with no merger signatures. Be sure to choose ‘Non-Mergers Table’ as the ‘Data Source’.
- You might notice that this plot looks pretty similar to the plot for the full post-quenched galaxy sample, just with fewer galaxies.
What about post-quenched galaxies that show signatures of merger activity? Do they also show a similar mix of star forming galaxies and AGN?
- To find out, create a new Table, but this time under ‘Prompt’ type:
filter .Merging != ‘Neither’
- The ‘!=’ syntax stands for ‘Not’, which means this filter picks out galaxies that had any other response under the ‘Merging’ column (i.e, tidal tails, merger, both). Notice how there are 505 sources in this Table.
- Now create a BPT scatter plot for your ‘Mergers Table’.
- Make sure this plot has a similar xmin,xmax,ymin,ymax as your other plots to ensure a fair comparison.
- You might also compare histograms of log(NII/Ha) for the different subsamples.
What do you find? Do you notice the difference? What could this be telling us about our post-quenched galaxies?!
Before you get too carried away in the excitement, it’s a good idea to compare the post-quenched galaxy sample BPT results against the control galaxy sample.
This comparison with the control sample will tell you whether this truly is an interesting and unique result for post-quenched galaxies, or something typical for galaxies in general. You might consider doing this in a new Dashboard, as I have, to keep things from getting too cluttered. In that new Dashboard, click ‘Data’, choose ‘Quench’ in the pop-up options, and choose ‘Quench Control’ as your data to upload. Now repeat Steps 1-4.
Do you notice any differences between your control galaxy and post-quenched galaxy sample results? What do you think this tells us about our post-quenched galaxies?
Stay tuned for Part 4 of this How-To-Guide. I’d love to build from your results from this stage, so definitely post the URLs for your Dashboards here or within Quench Talk and your questions and comments.
It was amazing how quickly the new Quench classifications were completed. We posted them on Friday and you were already done by Sunday morning. Wow, that’s awesome! This means we can turn our full attention to making sense of the data. And we need your help!
In Part 1 of this How-To-Guide to data analysis within Quench, you learned how to use Tools, our analysis platform, and were inspired (or so I hope) about ways to play with the data as you read the background literature about post-quenched galaxies and galaxy evolution.
In Part 2 of this How-To-Guide, we’re going to help you navigate using Tools to compare results from galaxies *you* classified with the rest of the post-quenched galaxy sample.
You’re 12 small steps away from your first comparison plot between your galaxies and the full sample… let’s get started!
Step 1: Enter Tools and log in using your Zooniverse login information.
Step 2: Choose ‘Quench’ in the pull-down menu in the upper-left, next to the words ‘zootools’. Now click ‘Create Dashboard*’ in the upper-right, and give it a name, like: ‘My Data in Context’.
Step 3: Click ‘Data’ in the upper-left and choose ‘Zooniverse’ in the pop-up options.
Step 4: In the window that pops up, choose ‘Recents’ or ‘Collections’. Your choice.
If you classified galaxies in quench.galaxyzoo.org, they’ll be accessible through ‘Recents’. Choose the max number possible. If you created a Collection of interesting galaxies in Quench Talk or want to look at someone else’s Collection, you can access them by clicking ‘Collections’.
I’ve created a Dashboard* in Tools called ‘Example: My Data in Context’. Take a look and, if you’d like, you can even make edits by copying it into your Tools environment.
In my Dashboard ‘Example: My Data in Context’, I chose ‘Collections’. I love #Quencher SUMO_2011’s Collection of ‘Blue’ galaxies from Quench. If you go to that URL, the Collection ID is listed after the final ‘/’ in the URL. In this case, the Collection ID is CGSS00000x. I inputted that ID into the pop-up window in Tools, in the box next to ‘Enter Collection Id:”. I then clicked on ‘Import Data’.
Step 5: Now that you have your galaxies’ information imported into the Dashboard, it’s time to play with them. Click on ‘Tools’ in the upper-left and choose ‘Table’ in the pop-up options.
Step 6: In your Table window, choose ‘Zooniverse-1’ in the pull-down menu under ‘Data Source’. Now the Table knows to work with that set of data.
Step 7: As in Part 1 of this How-To-Guide (https://blog.galaxyzoo.org/2013/08/23/quench-boost-a-how-to-guide-part-1/), you’ll make a new column that has color information about your galaxies. You do this by subtracting the brightness of your galaxy in one filter from the brightness of your galaxy in another filter.
In the open space under ‘Prompt’ in your Table, write: field ‘My Galaxies Color u-r’, .u-.r
If you scroll to the right in your table, you’ll see that you created a new column of information, called ‘My Galaxies Color u-r’.
Step 8: Click ‘Tools’ in the upper-left and choose ‘Scatterplot’ in the pop-up options.
Step 9: In your Scatterplot window, choose ‘Table-2’ in the pull-down menu under ‘Data Source’. Now the Scatterplot knows to work with the Table, which includes your new column with Color information.
Step 10: Choose ‘log_mass’ for the X-axis and ‘My Galaxies Color u-r’ for the Y-axis. Recent star formation is seen strongly in the u-band while older stars dominate the r-band. The color, u-r, tells you about the star formation history for each of your galaxies. Check out this post for more details.
Step 11: How do your galaxies compare with the full sample of post-quenched galaxies? To answer this, we redo the steps 3-10 above, but for the post-quenched galaxy sample.
- Click on ‘Data’ in the upper-left and choose ‘Quench’ in the pop-up options.
- Click on ‘Quench Sample’ in the pop-up window.
- Click on ‘Tools’ in the upper-left and choose ‘Table’ in the pop-up options.
- In the new Table window, choose ‘Quench-4’ in the pull-down menu under ‘Data Source’. This loads the Quench Sample into that Table.
- In the open space under ‘Prompt’ in your Table, write: field ‘Quench Galaxies Color u-r’, .u-.r
- Click on ‘Tools’ in the upper-left and choose ‘Scatterplot’ in the pop-up options.
- In the new Scatterplot window, choose ‘Table-5’ in the pull-down menu under ‘Data Source’.
- Choose ‘log_mass’ for the X-axis and ‘Quench Galaxies Color u-r’ for the Y-axis.
- Zoom in on the data, for example, choosing Xmin: 7, Xmax: 12, Ymin: 1, and Ymax: 4.
Step 12: Place your two scatterplots side by side. For a fair comparison, make sure the x- and y-axis range is the same for both plots, otherwise the stretch might skew your analysis. I tend to make the axes ranges in the plot showing My Galaxies match the plot showing the Quench Sample.
What do you notice about your subsample of post-quenched galaxies compared to the full sample? Do they occupy a particle sub-space within the plot? Or are they randomly distributed throughout the quench space?
The figure below shows what you’ll see if, like me, you uploaded SUMO_2011’s Collection of blue galaxies. You’ll notice that all of the blue-collection galaxies are way bluer (closer to the bottom of the plot, near values u-r = 1.5) than the full post-quenched galaxy sample (which spread from u-r values of 1 to u-r values of 3.5 and higher). This is a reassuring reality check given what you see visually when you look at the color of the galaxies. The plot also tells us that since these blue galaxies have such low values of ‘u-r’, they’ve had more recent star formation than most of the post-quenched galaxies.
In looking at these two plots side-by-side, I wondered: Why are there so few massive post-quenched galaxies (log_mass > 11) with bluer colors (u-r < 2.0)? If we compare our post-quenched galaxies with our control galaxies, do I see any difference? Specifically, are there massive (log_mass > 11) control galaxies with bluer colors (u-r < 2.0)? If there are, what might that be telling me about our post-quenched galaxy sample?
Stay tuned for Part 3 of our How-To-Guide for taking part in the analysis phase of the research process. If you have suggestions for what you’d like to learn more about, please post here. Thank you all, and keep on Quenching!
*Dashboard is the place within Tools (tools.zooniverse.org) for volunteers to observe, collect, and analyze data from Zooniverse citizen science projects.
Experience Science from Beginning to End! Classify, Analyze, Discuss, and Collaboatively Write an Article!
Galaxy Zoo and other Zooniverse projects have given thousands the opportunity to contribute to scientific research. It’s time to take the role of volunteers to the next level. For the next two months*, this new Galaxy Zoo Quench project provides the opportunity to take part in the ENTIRE scientific process – everything from classifying galaxies to analyzing results to collaborating with astronomers to writing a scientific article!
Galaxy Zoo Quench will examine a sample of galaxies that have recently and abruptly quenched their star formation. These galaxies are aptly named Post-Quenched Galaxies. They provide an ideal laboratory for studying how galaxies evolve from blue, star-forming spiral galaxies to red, non-star-forming elliptical galaxies. Using the more than a million galaxies in the Sloan Digital Sky Survey, we identified ~3000 post-quenched galaxies. By classifying these galaxies and analyzing the results, we will explore the mechanisms that quenched their star formation and investigate the role of post-quenched galaxies in galaxy evolution.
The entire process of classifying, analyzing, discussing, and writing the article will take place over an 8 week period*, beginning July 18th. After classifying the galaxies, volunteers will use the tools available within Zooniverse to plot the data and look for trends. Through reading articles and interaction in Talk, volunteers will gain background information. Throughout, they’ll discuss with the science team their interpretation of the results. At the end of the process, volunteers and the science team will collaboratively write a 4-page Astrophysical Journal article.
What causes the star formation in these galaxies to be quenched? How do interactions impact galaxy evolution? What is the fate of our Milky Way? Join us this Summer (or Winter if you’re below the equator!) in exploring these questions, being a part of the scientific process, and contributing to our understanding of this dynamic phase of galaxy evolution!
CLICK HERE TO PARTICIPATE!
We’ll be sharing more details about this project during the next Galaxy Zoo Hangout, on Monday, July 15th at 14:00 CST / 19:00 GMT / 20:00 BST. Have questions about the project? Post them here or tweet at us (@galaxyzoo). Just before the Hangout starts, we’ll embed the video here so you can watch from the blog.
The best way to send us a comment during the live Hangout is through twitter (@galaxyzoo). You can also leave a comment on this blog post, or on Google Plus, Facebook or YouTube. See you soon!
Update: here’s the hangout (and the mp3 version)!
*Note: science timelines often subject to a factor of two uncertainty. We’ll do our best to keep on track, at the same time expecting the unexpected (all part of the fun of doing science!).
I swear we are consistently trying to keep our live hangouts to about 15 minutes. We have so far failed at keeping to time, but hopefully also succeeded in the sense that we only run over because there’s so much to discuss.
We had a number of good questions from Twitter, Facebook and the blog about various types of galaxies — from red spirals to green peas and blue ellipticals — and I rather arbitrarily decided this was an indication that our hangout should have a color theme. That is, what exactly does “color” mean in the context of astronomy? What is going on physically when a galaxy is one color versus another, or has multiple colors? Is color information always telling us the same thing? We tried to address all those questions, as well as show some examples of different galaxies in the above queried categories. As a bonus, we learned how galaxy colors are related to the town my grandparents retired to. (This post’s title is a quote from the Green Valley Chamber of Commerce’s official website.) That was as much a surprise to me as it was to the viewers!
We also talked about what’s currently going on in Galaxy Zoo behind the scenes. Earlier today, Kyle sent around a really nice draft of the Galaxy Zoo 2 data paper for the team to read and comment on (you’ll have to watch the video to get a sneak peek at some of the figures).
And it’s that time again: Hubble Space Telescope proposals are due in about a week. We talked about the proposal process from concept to submission to review, discussing both specifics of certain telescopes and the general practices that (we hope) help lead to a successful proposal. Here’s a hint: it may not be what you think!
We covered all this and some other questions, too. No wonder we ran a little over…
And here’s the podcast version: