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Galaxy Zoo Continues to Evolve

M31 at ultraviolet, optical, mid-IR wavelengths

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.

SDSS 587722982831358015 in optical and infrared from UKIDSS

Even though the optical SDSS image (left) is deeper than the near-IR UKIDSS image (right), you can still see that the UKIDSS image is less affected by the dust lanes seen at left.

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.

redshifted galaxy images

1 galaxy, 4 redshifts.

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.

Evolutionary Paths In Galaxy Morphology: A Galaxy Zoo Conference

Evolutionary Paths in Galaxy Morphology Conference Photo

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.

Chris Lintott Galaxy Zoo

There’s a lot to that legacy already, and it’s still being written.

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.

Karen Masters Galaxy Zoo Secular Evolution

Karen explains her simple and clear diagram showing different galaxy evolutionary processes.

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:

Day 1  |  Day 2  |  Day 3  |  Day 4

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):

(click here for the podcast version)

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.

Galaxy Zoo Team in Sydney

Left to Right: Tom, Kevin, Bob, Amit, Ed, Chris S, Bill, Kyle, Chris L, Ivy, Brooke, Karen, Julie

Quench Boost: A How-To-Guide, Part 4

Now that we’ve been initiated into the cool waters of Tools (Part 1), we’ve compared our *own* galaxies to the rest of the post-quenched sample (Part 2), and we’ve put your classifications to use, looking for what makes post-quench galaxies special compared to the rest of the riff-raff (Part 3), we’re ready for Part 4 of the Quench ‘How-To-Guide’.

This segment is inspired by a post on Quench Talk in response to Part 3 of this guide. One of our esteemed zoo-ite mods noted:

There are more Quench Sample mergers (505) than Control mergers (245)… It seems to suggest mergers have a role to play in quenching star formation as well.

Whoa! That’s a statistically significant difference and will be a really cool result if it holds up under further investigation!

I’ve been thinking about this potential result in the context of the Kaviraj article, summarized by Michael Zevin at http://postquench.blogspot.com/. The articles finds evidence that massive post-quenched galaxies appear to require different quenching mechanisms than lower-mass post-quenched galaxies. I wondered — can our data speak to their result?

Let’s find out!

Step 1: Copy this Dashboard to your Quench Tools environment, as you did in Part 3 of this guide.

  • This starter Dashboard provides a series of tables that have filtered the Control sample data into sources showing merger signatures and those that do not, as well as sources in low, mid, and high mass bins.
  • Mass, in this case, refers to the total stellar mass of each galaxy. You can see what limits I set for each mass bin by looking at the filter statements under the ‘Prompt’ in each Table.

Step 2: Compare the mass histogram for the Control galaxies with merger signatures with the mass histogram for the total sample of Control galaxies.

  • Click ‘Tools’ and choose ‘Histogram’ in the pop-up options.
  • Choose ‘Control’ as the ‘Data Source’.
  • Choose ‘log_mass’ as the x-axis, and limit the range from 6 to 12.
  • Repeat the above, but choose ‘Control – Merging’ as the ‘Data Source’.

The result will look similar to the figure below. Can you tell by eye if there’s a trend with mass in terms of the fraction of Control galaxies with merger signatures?

control_mass_mergers

It’s subtle to see it in this visualization. Instead, let’s look at the fractions themselves.

Step 3: Letting the numbers guide us… Is there a higher fraction of Control galaxies with merger signatures at the low-mass end? At the high-mass end? Neither?

To answer this question, we need to know, for each mass bin, the fraction of Control galaxies that show merger signatures. I.e.,

equation

Luckily, Tools can give us this information.

  • Click on the ‘Control – Low Mass’ Table and scroll to its lower right.
  • You’ll see the words ‘1527 Total Items’.
  • There are 1527 Control galaxies in the low mass bin.
  • Similarly, if you look in the lower right of the ‘Control – Merging – Low Mass’ Table, you’ll see that there are 131 galaxies in this category.
  • This means that the merger fraction for the low mass bin is 131/1527 or 8.6%.
  • Find the fraction for the middle and high mass bins.

Does the fraction increase or decrease with mass?

Step 4: Repeat the above steps but for the post-quenched galaxy sample.

You may want to open a new Dashboard to keep your window from getting too cluttered.

Step 5: How do the results compare for our post-quenched galaxies versus our Control galaxies? How can we best visualize these results?

  • In thinking about the answer to this question, you might want to make a plot of mass (on the x-axis) versus merger fraction (on the y-axis) for the Control galaxies.
  • On that same graph, you’d also show the results for the post-quenched galaxies.
  • To determine what mass value to use, consider taking the median mass value for each mass bin.
  • Determine this by clicking on ‘Tools’, choosing ‘Statistics’ in the pop-up options, selecting ‘Control – Low Mass’ as your ‘Data Source’, and selecting ‘Log Mass’ as the ‘Field’.
  • This ‘Statistics’ Tool gives you the mean, median, mode, and other values.
  • You could plot the results with pen on paper, use Google spreadsheets, or whatever plotting software you prefer. Unfortunately Tools, at this point, doesn’t provide this functionality.

It’d be awesome if you posted an image of your results here or at Quench Talk. We can then compare results, identify the best way to visualize this for the article, and build on what we’ve found.

You might also consider repeating the above but testing for the effect of choosing different, wider, or narrower mass bins. Does that change the results? It’d be really useful to know if it does.

Happy Tooling!

Quench Boost: A How-To-Guide, Part 3

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:

  1. The classification results for the 57 control galaxies that needed replacements have been uploaded into Quench Tools.
  2. 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).
  3. We’ve uploaded the emission line characteristics for all the control galaxies.
  4. We’ve uploaded a few additional properties for all the galaxies (e.g., luminosity distances and star formation rates).
  5. We corrected a bug in the code that mistakenly skipped galaxies identified as ‘smooth with off-center bright clumps’.

In Part 1 of this How-To-Guide to data analysis within Quench, you learned how to use Tools and were introduced to the background literature about post-quenched galaxies and galaxy evolution.

In Part 2 you used Tools to compare results from galaxies *you* classified with the rest of the post-quenched galaxy sample.

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!

merger2

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.

BPT

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.

What is a Galaxy? …the return

Abell S740 / HST

The first time I gave a public talk, I spent an hour describing why galaxy classification is fundamentally important to the study of the Universe, the origins of Galaxy Zoo, the amazing response of the volunteers and the diverse results from their collective classifications of a million galaxies near and far. I showed many gorgeous galaxy images, a few dark matter simulations and even a preview of the Hubble image of Hanny’s Voorwerp.

As I finished my talk and the Q&A began, I braced myself for the inevitably interesting and challenging questions (I seem to get a lot of questions about black holes and spacetime).

A brief pause, and then the first question echoed from somewhere in the darkened auditorium: …”What’s a galaxy?

Oops. Apparently I’d forgotten that little detail at the start of the talk. So I described a typical galaxy (if there is such a thing): a collection of stars, gas, dust, dark matter, all gravitationally bound together. Then I made a joke about scientists forgetting to define their terms, and we moved on to the next raised hand.

Turns out, though, it’s not such an easy question. Even though my casual definition works fine for most galaxies, it’s not at all an agreed-upon standard. We’ve discussed this on the blog before, and even in the short time (astronomically speaking) since Karen wrote that very nice post, more work has been done to find galaxies that push the boundaries and force us to re-think what it really means to be a galaxy.

Segue 1

The circled stars (plus a lot of dark matter you can’t see) are Segue 1, one of the smallest galaxies we know about. To read more on this, click the image.

So, spurred by a very broad interpretation of a question left for us in the comments on the post announcing this hangout, we decided to re-visit the discussion, covering the various properties a galaxy must have, should have, could have, and can’t have. We discussed the smallest galaxies, found by counting and measuring each of their individual stars. We discussed the biggest, brightest galaxies in the universe, living in rich environments and grown fat by eating other galaxies. And everything in between.

(podcast version here)

Note: when we talk about Segue 1 and 2, I say that these galaxies are unique because they have low mass-to-light ratios. Despite the pause that indicated I was trying to keep from inverting numerator and denominator… that’s exactly what I did. The galaxies have very few stars compared to the amount of dark matter in them, so their mass is high and their light is low, so their mass-to-light ratios are high. Oops (again)!

Clicking 10 Billion Years Into The Past

Astronomers use funny units. We have the light-year, which sounds like a time but is actually a distance. There’s the parsec, a historical (but still used) unit of distance that was famously mis-used as a time in Star Wars. And then there’s redshift, which is actually a velocity — distance divided by time — but which, because of the expansion of the universe, astronomers get to use as a proxy for distance. 

While it may be convenient for us to use distance units where we set a mind-blowingly large number equal to 1, it doesn’t really help us communicate our work to the public. If I note that the galaxy images from CANDELS look a little different from the galaxies in the SDSS because the CANDELS galaxies are typically at a redshift of 2, that’s pretty meaningless. But it’s a little different to think of the fact that, when you classify a galaxy from CANDELS, you may be looking three-quarters of the way to the edge of the visible universe, and seeing the galaxy as it was 10 billion years ago. 

Okay, that's kinda cool.

Okay, that’s kinda cool.

During this hangout, we announced that your clicks and classifications of the CANDELS galaxies have been moving at such an impressive rate that the first round is finished. Every galaxy has enough classifications for us to get a very good sense of what its morphology is. It may be that, for some of the galaxies where there are clearly more details to flush out, we will ask for a few more classifications per galaxy. And there will probably be future CANDELS images from survey fields that are still being completed. So, don’t worry, there will still be plenty of opportunities to classify galaxies as they were 10 billion years ago!

In the meantime, though, we’re getting ready not just to do the scientific analysis, but to share Galaxy Zoo results with our colleagues around the world. The summer conference season is upon us, and many of us have given and are giving talks and posters at various meetings in various cities. This includes not just the recent meeting highlighting the importance of galaxy morphology in the era of large surveys at the Royal Astronomical Society and the upcoming ZooCon in Oxford and Galaxy Zoo meeting in Sydney, but also several more general conferences, including the 222nd American Astronomical Society meeting and the upcoming UK National Astronomy Meeting. Spreading the word about the scientific results we’re finding with Galaxy Zoo is one of the most important parts of our job — and it doesn’t hurt that in order to do that we have to visit some very interesting places. During the hangout we chatted a bit about that and also took some of your questions:

Note: although it was a beautiful sunny day in Oxford, the variable audio quality is not because I was occasionally distracted looking out the window. I don’t think it was the new microphone, either. We’ll look into it, but in the meantime I’ve tried to equalize the podcast version with some after-editing, so hopefully that is slightly better.

Using Space Warps to Discover and Weigh Galaxies

A mosaic of the Space Warps spotter's guide.

John Wheeler once summarized General Relativity as “Matter tells space how to curve, and space tells matter how to move.” While that is a handy description, and while there have been many textbooks written, lectures given and websites constructed to explain this, the quote itself doesn’t address what happens to the light streaming through the universe as it encounters the warped space curved by matter.

A useful visualization.The simple answer is: it curves too, and Einstein’s equations provide predictions for exactly how it works. In fact, observations of the bending of starlight around the Sun were one of the first implemented tests of General Relativity, and it passed with flying colors. On the scale of the Universe, the Sun isn’t that massive, but it’s massive enough to bend the light just a little, and by exactly the amount the equations predicted.

Those equations say that more matter in the same place is more likely to produce a strong lens effect, distorting and magnifying a background source. So what happens when you have a *lot* of matter, say, in a big galaxy or a cluster of galaxies?

a) an Einstein cross (credit: NASA/ESA); b) an example from the Space Warps dataset; c) a known lens in CANDELS that Galaxy Zoo users spotted.

From left to right: a) an Einstein cross (credit: NASA/ESA); b) an example from the Space Warps dataset; c) a known lens in CANDELS that Galaxy Zoo users spotted.

Some pretty impressive configurations, which are rare but which humans are best at finding — hence Space Warps, the Zooniverse’s newest project and our astronomical project sibling. Co-lens-experts Phil Marshall and Aprajita Verma joined us during this hangout to describe how they use gravitational lenses to weigh galaxies. In particular, they can tell the difference between Dark Matter and “matter that’s dark” — the former being the exotic particles that are very different from stars and gas and planets and people, and the latter being normal matter that isn’t bright, such as brown dwarf “stars” that never actually ignited.

Note: Google+ was feeling a bit out of sorts, so the first minute or so of the broadcast was cut off, during which time Bill Keel showed us the first known image of a gravitational lens, from 1903. We went on to talk about all of the above, and more besides, including the importance of simulated lenses, why the images Space Warps uses are specially tuned to help us find lenses, and how the science team (which includes citizen scientists from Galaxy Zoo!) plan to turn our clicks into discoveries.

(or download the podcast mp3 here)

Notice my swapping of pronouns to “we” — I’m not on the Space Warps science team, but I’ve done nearly 100 classifications now myself! I can’t wait to see the results start to come in from this project.

Oh, Sweet Spiral Of Mine

Herschel, IRAS, and optical images of Orion's star-forming complex at multiple scales and multiple wavelengths.

See the video of our latest hangout here (or, if you prefer, click to download the podcast version):

Spiral galaxies are seemingly endless sources of fascination, perhaps because they’re so complex and diverse. But why does spiral structure exist? Why do some spiral galaxies have clearly defined spiral arms and others have flocculent structure that barely seems to hold together? What’s the difference between a 2-arm spiral and a 3-arm spiral? How many kinds of spirals do we actually observe? And what is happening to the stars and gas in spiral galaxy disks?

M81 spiral galaxy - panchromatic slide

Clockwise from top right: X-ray, UV, optical, near-IR, mid-IR, far-IR, radio

All of the above questions are related to a question we got right at the end of our last hangout: what is the significance of the number of spiral arms? Determining how many spiral arms a galaxy has is hard, and is often subjective — so why bother?

It’s a good question. Part of the reason spiral arm classification & count is a challenge is that it often depends on the wavelength at which you observe a galaxy. New stars tend to form along the spiral arms, whereas older stars have time to spread out into more uniform orbits. So ultraviolet observations of a galaxy, which tend to pick out the new and bright stars, often highlight the spiral arms much more strongly than longer-wavelength observations, which see more light from older stars.

It’s not quite that simple, though. As you get to longer and longer wavelengths, you start to pick up the heat radiated by clouds of gas and dust, which are often stellar nurseries — and often trace spiral arms. At a wavelength of 21 centimeters you can detect neutral Hydrogen, which provides raw material for the cooling and condensation of gas into cold, dense molecular clouds that form stars in their densest pockets. Each wavelength you observe at provides a glimpse at a different targeted feature of a spiral galaxy.

Milky Way HI map

A map of neutral Hydrogen in the Milky Way — complete with yellow “you are here” arrow.

Including our own, of course: we live in a spiral galaxy (though how many arms it has, and whether it’s flocculent, is a matter of debate), and it provides the best means of studying star formation up close. When studying other galaxies, it’s easy to get caught up in the race to discover the biggest, the smallest, the farthest and the most extreme, and forget that our own Universal neighborhood is pretty amazing too.

Horsehead Nebula in Herschel and HST images

Herschel sees much longer wavelengths than HST, so its resolution isn’t as high even though it has a bigger mirror. (Click to see a larger version.) Credit: ESA/NASA

For example, one of the most famous nebulae in the world was recently imaged by one of the most famous telescopes in the world — again — but this time in the near-infrared. The Horsehead Nebula is a well-known feature in the Orion star-forming complex, and the new Hubble images provide a great opportunity to learn even more about this region that has been studied for hundreds of years. How thick and cold is the gas and dust in the nebula? How long will it take for it to dissipate under the harsh radiation of the bright, young stars near it? What’s going on behind it?

The near-infrared view from HST is sort of the sweet spot in this spectacular space — the wavelengths aren’t so long that the resolution suffers, but they are long enough that you see through a bit more of the clouds than in the optical. So you see more of the structure of the cloud itself, and more of where it’s thin and thick. If you zoom in, you can even see distant galaxies peeking through! And not just on the edges: in some parts you can see galaxies through the middle of the nebula. Wow. This image alone contains spiral galaxy insights big and small, near and far, from the very distant universe and right in our own backyard.

Note: right at the end of the hangout, we again got another great question from a viewer that we didn’t have time to answer. So stay tuned for the next hangout when we just might have a thing or two to say about dark matter, dark energy and new projects!

Two Atoms Populate on a Dust Grain

Dusty Nebula

I enjoy days where we get to use questions from the public to meander our way through the Universe. Our latest live hangout saw us discussing the latest update to the Galaxy Zoo site — made based on your clicks! — and doing a live, collective classification on a few example objects from our Hubble sample that we hope represent the kind of things you’ll be seeing more of from now on.

We debated, for example, whether this galaxy’s central “feature” was a bulge or a bar:

bar versus bulge? overlap versus merger?

We also discussed whether these galaxies are merging or overlapping.

Whether this relatively featureless galaxy’s blue smudge indicates a voorwerp:

GDS_13741

Depending on the redshift, a voorwerp in Hubble could be blue, green or red.

And how many spiral arms this galaxy has:

GDS_13741

Also, these two galaxies may have roughly the same proportions between them as between the Milky Way and the Large Magellanic Cloud.

We also talked about the origin and importance of dust in galaxies, and just what a green pea would look like in the Hubble data. Green peas are galaxies with incredibly high rates of star formation. They’re rare in the local Universe, but how rare do we think they were billions of years ago, at the epoch we’re looking back to with Hubble?

GDS_4792

Thing is, a “green pea” at redshift z=2 would be bright red.

And, for that matter, what were the stars like then? Astronomers very broadly group stars into three populations depending on their composition. The very earliest stars were made from the primordial elements forged during the Big Bang — almost entirely Hydrogen and Helium, nearly devoid of anything else (we call “anything else” a metal, including elements like Carbon and Oxygen). The next generation of stars had some metals, but the Universe has been around long enough that those stars (even the lower-mass ones that live for a long time) are past their prime and a new generation, one with compositions generally like our Sun, are now in their heyday.

Naturally, though, since the Sun is our First Star, we call its generation Population I. The slightly older stars, many of which are still around and living in our galaxy and others, are Population II; and the very massive rockstars of the early universe that have all died out are called Population III. So “Pop III” were the first stars — a slight reversal, but labels and names that seemed like a better idea at the time than with hindsight are nothing new in Astronomy. (Exhibit A: the magnitude system. Exhibit B: “planetary nebula“.)

Bonus: green peas, voorwerpjes, and planetary nebulae are just three of the phenomena that (at least in part) glow green to human eyes because of one particular frequency of light emitted by Oxygen at a certain temperature, an atomic transition seen only rarely on Earth but fairly often in the Universe.

Planetary Nebula IC 1295 - ESO

This VLT image shows the planetary nebula IC 1295 in ghostly green. Image Credit: ESO

Also, did you know that dust grains are the singles bars of the atomic universe, allowing atoms to meet and combine into molecules and cooling the gas clouds they live in — which in turn helps new stars form? Heating and cooling, gravity and pressure, and the interplay between atoms, molecules, and radiation are all a part of what gives us this amazingly diverse Universe. We understand quite a lot of it given that we are such a tiny part of it, but what we know is dwarfed by what we don’t. And that’s just the way astronomers like it… we love a challenge and we’re glad to have as much help as possible sorting things out.

Here’s the hangout video:

And click here to listen to the mp3 podcast version.

Blood Oranges are just like Hubble Galaxies

Hubble CANDELS zoom

Astronomers always want better images. Sometimes it’s possible right away; other times doing better requires new technology and/or waiting for the next generation of telescopes. We have both kinds of “fuzzy blobs” in Galaxy Zoo, and during this hangout we showed several examples. For a couple of hangouts now we’ve been meaning to address some of the most frequently asked questions about the faintest, most distant galaxies we ask volunteers to classify:

  • what are they?
  • why are the images so fuzzy?
  • can we get better images of them now or in the future?

Given the data we have, the short answer to the first question is that we don’t yet know for sure — and, perhaps most importantly, we don’t need to know all the details. We can learn quite a lot from classifying even faint, fuzzy objects. Some of the faint galaxies on Galaxy Zoo are among the most distant galaxies ever imaged by the Hubble Space Telescope, and we don’t necessarily expect them to look like galaxies we see more nearby, so classifications from our volunteers are helping us to understand them even when we don’t have all the information we might want.

And what would it take to give us the information we want? What’s the future of astronomy after Hubble? How can we get better data than we have right now? Do we need to go into space to do it? (And what else are we working on right now, anyway?) Answers given in the video:

This is a great time to be working on Galaxy Zoo: there’s plenty to classify and analyze, and — of course — plenty to discuss. So stay tuned for next time!

Note: for those who prefer audio only, here’s a link to the podcast version.

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