Brut uses the catalogue of bubbles identified by more 35,000 citizen scientists from the original Milky Way Project. These bubbles are used as a training set to allow Brut to discover the characteristics of bubbles in images from the Spitzer Space Telescope. This training data gives Brut the ability to identify bubbles just as well as expert astronomers!
The paper then shows how Brut can be used to re-assess the bubbles in the Milky Way Project catalog itself, and it finds that more than 10% of the objects in this catalog are really non-bubble interlopers. Furthermore, Brut is able to discover bubbles missed by previous searches too, usually ones that were hard to see because they are near bright sources.
At first it might seem that Brut removes the need for the Milky Way Project – but the ruth is exactly the opposite. This new paper demonstrates a wonderful synergy that can exist between citizen scientists, professional scientists, and machine learning. The example outlined with the Milky Way Project is that citizens can identify patterns that machines cannot detect without training, machine learning algorithms can use citizen science projects as input training sets, creating amazing new opportunities to speed-up the pace of discovery. A hybrid model of machine learning combined with crowdsourced training data from citizen scientists can not only classify large quantities of data, but also address the weakness of each approach if deployed alone.
We’re really happy with this paper, and extremely grateful to Chris Beaumont (the study’s lead author) for his insights into machine learning and the way it can be successfully applied to the Milky Way Project. We will be using a version of Brut for our upcoming analysis of the new Milky Way Project classifications. It may also have implications for other Zooniverse projects.