I posted a request yesterday afternoon for help in sorting out a set of possible mergers; as the developers are busy with Zoo 2, I just posted a set of lists. Clunky, and annoying, but I’d hoped people would still be able to help out. By the end of last night, forum member Waveney had built us a proper interface. You’ll find the address at the end of the instructions below.

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One of the most impressive aspects of Galaxy Zoo has been the way we’ve been able to use the data in all sorts of unexpected ways. Whether it’s finding overlapping galaxies or strange blue blobs it’s become obvious that there is a lot more to all of your efforts than just elliptical vs spiral. When we were putting together the site, we didn’t give much thought to the many galaxy mergers in the catalogue; as far as the primary science goals went they were mostly just contaminants in our data set.It quickly became obvious that we had the chance to assemble a large and interesting set of mergers and learn more about why, where and how these beautiful collisions occur; conducting the first investigation into this is Daniel Darg who’s based here in Oxford.

He’s already got interesting results, but we’d like his paper to be as comprehensive as possible and that means including as many mergers as we can find. Everything which has a weighted vote of more than 60% in the merger category has proved to be a true merger, but now we need for help to have a closer look at those which have a vote between 40% and 60% in this category. We’re keen to get on with this, so rather than wait for Zoo 2 we thought we could ask for your help here. To find out how, read on below.

Update : New, easier classification system available : See this post.

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We’re only a couple of weeks from the observing session to look at some of the overlapping galaxies pointed out by the good people of Galaxy Zoo, so it’s high time for us to think about how to do this most effectively. We are scheduled for the nights of April 25-29, using the 3.5-meter WIYN telescope at Kitt Peak, Arizona. The timing is just right for the SDSS sample, since the northern galactic hemisphere that it covered most completely is up all night this time of year and the moon is waning and moving out of the way. Here are some outside and inside views of the telescope:

You can even see the current weather at Kitt Peak (as long as it’s daylight there) using this webcam view – WIYN is at upper right, on the mountain horizon directly above the nearby University of Arizona telescopes.We’ll be using a fairly new CCD camera called OPTIC, which is visiting from the University of Hawaii. This camera has chips with a special architecture allowing the accumulating image to be moved around on the chip in any direction by purely electronic means, so it can keep up with atmospheric motions as long as there is a bright enough guide star in the right field of view. The advantage of doing this is that electrons can be moved around much faster than the whole telescope, and without introducing any mechanical vibrations. This device will help us work out how far out in redshift we can pursue galaxies for this project using ground-based telescopes.

Now – which galaxy pairs do we start with? You all have furnished a magnificent sample, something like 875 overlapping pairs that look useful for dust measurements by the time I fold in the last few weeks’ harvest. I keep my target book for this project as a set of PDF files accessible from anywhere – for those who really want to see, the list is broken into four parts starting at right ascension zero, 10 hours = 150 degrees, 13 hours = 195 degrees, and 15 hours = 225 degrees. (These are really cool to flip through rapidly page by page). This huge sample gives us the luxury of being able to select our targets carefully, making sure to span ranges of foreground galaxy type and luminosity. An obvious starting point is the nearest galaxies, where we can see the most detail in the dust. We want to include pairs with both elliptical and spiral background galaxies; the dust detail is clearest when the background galaxy is smooth, but we also want to correlate with ultraviolet data from the GALEX satellite, and only spirals are bright enough in the UV to do this sensibly.If we think about choosing galaxies based on a score, we give them points for being bright and nearby, points for having two known and quite different redshifts, and points allocated according to how long the GALEX UV sky survey looked at that piece of the sky.

But we also want to pick a few higher-redshift galaxies, at z=0.1 or greater, to show just how well we can measure their dust from the ground. (Recall that we do have pending Hubble proposals to do some of these, but there are something like 960 competing proposals for next year’s observations and the oversubscription will be fierce).Our goal is to look at something like 50 pairs for an hour each over the 5 nights, and naturally we want to start with the most interesting ones in case the weather goes downhill. Chris will be blogging during the observing run, and certainly we’ll be getting action pictures. There are also plans for a writer to watch the action for a night and report the scene from a non-astronomer’s perspective. We hope to be able to do some analysis near real-time and get dust maps to show how well the data match our needs; if so you’ll be seeing some. I’m attaching a couple of images showing this on NGC 5544/5, which has been posted a couple of times in different threads (but from older data here). First the blue-light image shows the overall geometry. Then we have subtracted a symmetric model for the light from the foreground galaxy and divided by a symmetric model for the light of the background galaxy (both models being based on the data in the non-overlapping regions), giving a map of the absorption by dust in the overlap area. We hope to get a lot more of these.

As some of you may have noticed, our first paper has caught the eye of New Scientist (in fact they have written about us before). This is pretty cool considering that we effectively had a null result in our paper – concluding that the Universe seems to be relatively normal. The real excitement is due to the ‘people power’ that the Zoo has harnessed.

I think you need a subscription to see the full article, and however much I’d love to give out Chris’ account details I am sure this breaks certain rules! However, if you have been able to take a look at the full piece then you may be a little curious about the comments from Prof Michael Longo towards the end

It turned out people have a preference when picking orientation: despite the mirroring, 52 per cent of the galaxies were still described as anticlockwise. “Rather than the universe being odd, it might be that people are odd,” says Land. The team has submitted the findings to Monthly Notices of the Royal Astronomical Society (www.arxiv.org/0803.3247).

Longo, however, is unconvinced. The mirroring analysis was only carried out for 5 per cent of the galaxies studied and he believes this sample is too small to justify rejecting the original excess that users spotted, which corroborated the existence of the axis. “[Land and colleagues] have done an impressive job of organising the Galaxy Zoo project, but I believe their analysis is flawed,” he says.

It is really thanks to him that this part of the Zoo, looking into the spins of galaxies as opposed to just the morphology, took place (see here for more on the motivation of this part of the study). And he raises an interesting point in the article that I thought it’d be worth responding to…

The point was raised that because we only did the bias study on about 5% of the Galaxy Zoo sample then we cannot really comment at all on the level of bias in our full dataset. Indeed this sounds quite reasonable. We can see how the classifications for this random ~5% of the data behave when we flip the images, but how do we know that the full sample wouldn’t behave differently?

Well, I think there are two important points to be aware of. Firstly, with statistics we were able to confidently detect a bias in the classifications of these ~50,000 galaxies. The analysis we performed is discussed in some detail on this blog. It is a bit technical, but not only do we detect a bias effect, but with a method called resampling we further established the uncertainty in this result – the probability that the effect could just appear by chance.

For this the data was split into further subsets, and by looking at how the results varied between these groups we could estimate the overall uncertainty in our results. For example – if it turns out that removing a few pieces of information causes the results to vary wildly then this means that you have a huge uncertainty and cannot make strong final conclusions about the full dataset. In our case this method actually returned relatively small errors because even between subsets of the data the results did not vary much. When we formally computed the uncertainty (we used the jackknife method to be specific) we are able to detect the bias at the ‘3-sigma level’.

This kind of lingo is used a lot by scientists, and what we mean by ‘sigma’ is one standard deviation. This is a measure of how much numbers can be expected to vary by chance. Consider for example that you toss a fair coin a thousand times, and you want to know how many times you can expect to return a heads. Well obviously you’d expect 500 heads – but not necessarily exactly 500 as there will be some natural variance in the results. In this example it actually turns out that the number of heads you expect roughly obeys a Normal Distribution with mean of 500, and standard deviation of ~16. This means that if we repeated the experiment a number of times we expect 68.3% of the results to find the number of heads to be with 1 standard deviation of 500 (between 484-516), 95.4% within ‘2 sigma’ (468-532), and 99.7% within ‘3 sigma’ (452-548). If your experiment returned 450 heads from 1,000 tosses of the coin, then this would be unexpected at the ‘3-sigma level’ and would be highly unlikely – thus indicating that the dice is probably biased.

Well, similarly we found that our original and our flipped classifications were inconsistent at more than 3 standard deviations – and this means we can be sure at the 99.7% level than there is a bias effect in our study. This is what we mean by confidently!

But what about the full dataset? Well this is the second point – the bias-study galaxies were selected completely at random from the full dataset in order to get a representative sample of them. We have conclusively shown that there is a bias in the way people classify galaxies and hence the same effect should be present in the full sample. We cannot be 100% sure that the full sample would show exactly the same bias effect, but we can be over 99.73% sure (3 sigma) sure. In other words, for the bias effect to be a statistical fluctuation due to reanalyzing just 5% of the data, we would have to be very lucky (not quite LOTTO lucky, but more than BINGO lucky!). But once the bias effect is taken into account, the axis (or more specifically the excess of anti-clockwise galaxies) disappears. Alas!

For those of you who enjoyed our coverage of the AAS meeting back in January, I’m helping provide similar coverage of the UK’s National Astronomy Meeting currently being held in Belfast. The blog is here – come and join us.