A highlight of group meeting today was Sander Dieleman (Ghent) explaining to us how he won the kaggle Galaxy Zoo challenge. His blog post about it is great too. We hope to use something like his technology (which he also applies to music analysis) to Kepler light-curves. The convolutional trick is so sensible and clever I can't stand it. His presentation to my group reminds me that no use of deep learning or neural networks in astronomy that I know about so far has ever really been sophisticated. Of course it isn't easy!
Ness and I spent the full afternoon working on The Cannon, although a lot of what we talked about was context and next projects, which informs what we write in our introduction and discussion, and what we focus on for paper 2. One thing we batted around was: What kind of projects can we do with these labels that no-one has done previously? I was asking this because we have various assumptions about our "customer" and if our customer isn't us, we might be making some mistakes. Reminds me of things we talked about at DDD.