UPDATE (April 27th): the paper is now available on the journal website: http://mnras.oxfordjournals.org/content/450/2/1441

Together with Kyle Willett, one of the organizers of the Galaxy Challenge, I’ve written a paper about my winning solution for this competition. It is available on ArXiv.

The paper has been accepted for publication in MNRAS, a journal on astronomy and astrophysics, but is also aimed at people with a machine learning background. Due to this dual audience, it contains both an in-depth overview of deep learning and convolutional networks, and a thorough analysis of the resulting model and its potential impact for astronomy research.

There is some overlap with the blog post I wrote after the competition ended, but there is a lot more detail and background information, and the ‘results’ and ‘analysis’ sections are entirely new (although those of you who have seen one of my talks on the subject may have seen some of the images before).

I am very grateful to Kyle Willett for helping me write the manuscript. Without his help, writing a paper for an audience of astronomers would have been an impossible task for me. I believe it’s crucially important that applications of deep learning and machine learning in general get communicated to the people that could benefit from them, in such a way that they might actually consider using them.

I am also grateful to current and former supervisors, Joni Dambre and Benjamin Schrauwen, for supporting me when I was working on this competition and this paper, even though it is only tangentially related to the subject of my PhD.

Original arxiv link: http://arxiv.org/abs/1503.07077

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