Royal Astronomical Society
RAS PN 10/42 - 27 May 2010
Galaxy Zoo: reproducing galaxy morphologies via machine learningScientists at University College London (UCL) and the University of Cambridge have developed machine-learning codes modelled on the human brain that can be used to classify galaxies accurately and efficiently. Remarkably, the new method is so reliable that it agrees with human classifications more than 90% of the time. The research will appear in a paper in the journal Monthly Notices of the Royal Astronomical Society.
There are billions of galaxies in the Universe, containing anything between ten million and a trillion stars. They display a wide range of shapes, from elliptical and spiral to much more irregular systems. Large observational projects – such as the Sloan Digital Sky Survey – are mapping and imaging a vast number of galaxies. As part of the process of using these data to better understand their origin and evolution, the first step is to classify the types of galaxies within these large samples. The 250,000 members of the public participating in the Galaxy Zoo project recently classified 60 million such galaxies by eye.
Now, a team of astronomers has used Galaxy Zoo classifications to train a computer algorithm known as an artificial neural network to recognize the different galaxy types. The artificial neural network is designed to simulate a biological neural network like those found in living things. It derives complex relationships between inputs such as the shapes, sizes and colours of astrophysical objects and outputs such as their type, mimicking the analysis carried out by the human brain. This method managed to reproduce over 90% of the human classifications of galaxies.
- Monthly Notices of the Royal Astronomical Society,
30 Apr 2010, DOI: 10.1111/j.1365-2966.2010.16713.x
arXiv.org > astro-ph > arXiv:0908.2033 > 22 Mar 2010