Tag Archives: data reduction

Creating a Bubble Catalogue

In recent weeks, I’ve spent much of my time figuring out how to use all of your drawings to determine where the bubbles are in the Spitzer data. About a month ago we had a breakthrough. Thanks to a lengthy conversation with MWP science guru Matthew Povich, I realised that one of the reasons it is so hard to determine where a bubble should be drawn is that sometimes there is no right answer! There are many bubbles in the MWP that people would disagree on how to draw – the reason is that there is often not necessarily a right answer to the question “where is the bubble?”.

An example of just such a bubble is shown below, with all user drawings shown next to it. You can see that this bubble just isn’t that easy to draw and that there are even two or three structures within the image that one could call a bubble. Instead of trying to make this fit a rigid one-bubble definition, we realised that we should be using the human ability to recognise patterns. After all – this is exactly what you are all so good at, and computers are sometimes not.

Myself and Matthew decided that what we should do in these instances is simply allow two (or even three) bubbles to be deemed as ‘real’. The inner, red structure is a kind of bubble, and so is the open-ended green bubble just outside of it. One could also perceive a third bubble just below and to the left of these, and many people appear to have drawn just that. (This is in addition the multitude of smaller bubbles around the edge, of course). Whatever catalogue is produced by our data reduction, it probably should include at least the first two structures if enough people drawn them.

This decision has made creating a cleaned bubble catalogue much easier. The data reduction process described in my February blog post is still the process I’m using, although it has been greatly refined. More importantly, since February an enormous number of new bubbles have been drawn and this means the averaging process produces better results. Below you can see some results of the latest efforts and hopefully you’ll agree that what is being produced is a good catalogue, based on what you have all drawn. For the sake of testing, I am using one 3-by-2 degree section of the data. This is the region +12 degrees from the galactic centre and contains several interesting and complex features – which makes it a good testing ground.

Below you can see the 3×2 degree tile on its own, with all of your 7,000+ bubbles drawn on top and with the resultant ‘cleaned’ bubbles as well. You can click on any of the images to see the full version.

I have also been looking into other techniques for extracting the bubbles as the crowd sees them. Below you can see just the raw bubble data, drawn by users for this tile. With the background removed, we can use a simple contrast ratio to create a threshold, which we use to cut-out the bubbles from the original image.

This is another method for extracting data, and although it is harder to define a rigid catalogue of bubbles using this method, it may still have use in mapping regions of star formation in our galaxy.

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Reducing the Data

I’ve spent much of the past two weeks messing about with different ways to reduce down over 200,000 bubbles (now almost 220,000) into a sensible catalogue. This gets very messy so I will try and explain what I’ve been up to in stages. This is a process called data reduction and for a citizen science, crowd-sourced project like the MWP, it can get complicated. I thought it may interested some of you to see where we currently are in the process of turning your clicks into results.

The key part of the data reduction problem is that we have a very large set of data – the massive number of bubbles that have been drawn – and need to decide which among them are ‘similar’ to each other. We need to keep some flexibility of our definition of similarity because right now, I’m not sure what ‘similar’ means.

Essentially, bubbles are ‘similar’ when two people draw a similarly sized bubble in a similar location. This is something that sounds remarkably easy to say but was hard to do well in code. Comparing 200,000 bubbles to each other is obviously computationally intensive.

Screen shot 2011-02-22 at 10.23.07

In the end I decided that since the size of bubbles was a consideration then I would move across the galaxy, looking on ever-decreasing orders of size. To do this I split the galaxy into 2×2 degree boxes and take each box in turn. In each box I see if there are bubbles here that are of the order of the size of the box (meaning they have a maximum diameter that is between a half- and a whole-box). If there are bubbles on that scale I run a clustering algorithm and pick out groups of these bubbles with central positions clustered to within one quarter of the box size. If a cluster is found, those bubbles are then saved and removed from the whole list. I then divide the box into four and repeat until no bubble are found.

Screen shot 2011-02-22 at 10.22.42

This method means that when a box contains no bubbles, we need not continue down in size scale, but when it does contain bubbles we always split and inspect the four child boxes. In this way we move through the galaxy, in ever-decreasing boxes, but in a fairly efficient manner.

We also have to perform the same analysis with an offset grid. This is exactly the same but making sure we catch bubbles that had fallen on the borders of boxes.

Once we have passed across the galaxy on all size scales, we need to make sure we’ve cleaned up the duplicates created by the offset grid. We do this by considering our newly created list of ‘clean’ bubbles and running through them in order of size. When we find bubbles of a similar size and location they are combined, according to the number of users that drew that bubble. This can be done more easily now that there are far fewer bubbles (in my tests we have dropped to around 5% of the initial number by this stage).

Results

My initial run only looked at bubbles in the longitude range 0-30 degrees. Below are three images, showing one image from the MWP set (one of my favourites as lots of people see it differently). You can the the image, as it is shown to MWP users. Below that you see, overlaid in blue, the original bubbles as drawn by the users. In the third image you can see the same, but this time displaying the ‘cleaned’ results. In the original set the bubbles all have the same opacity, such that when they pile up you can see the similarities. The cleaned set gives the bubbles opacities according to their scores (think more opaque bubbles mean more users drew them).

GLM011680081mosaicI24M1

mwp_test_all_bubbles

mwp_test_clean_bubbles

It should be noted that the cleaned image does not yet display arcs, but rather always shows an entire ellipse. This is because I am not yet including the bubble cut-outs (which you can make out in the middle image) in the data reduction. These will be included at a later time.

You can see that I’m still getting some duplication at the end of the process – I may need to sweep across the final catalogue looking for similar bubbles until I reach a convergence when all bubbles are ‘unique’. I have been experimenting with this with mixed results but will continue my efforts.

If you’re still reading, I look forward to reading your comments. As I continue to make adjustments and progress with this reduction, I shall blog the results again. Many members of the science team are also having a go at this problem and so the final result may be quite different in the end as we improve things. I hope that this is an interesting insight into some of what goes on behind the scenes of the MWP.