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Next-generation astronomical facilities such as the LSST and the SKA will be game-changers, allowing us to observe the entire southern sky and track changing sources in near real-time. Keeping up with their alert-streams represents a significant challenge - how do we make the most of our limited telescope resources to follow up 100000 sources per night?
The biggest problem here is classification - we want to find the really interesting transients and spend our time watching those. However, classification based on the initial survey data can only get you so far - we'll need to use robotic follow-up telescopes for rapid-response observations, to give us more information on the most promising targets. To get the most science done, we need to be smart about scheduling that follow-up.
We're exploring use of active learning algorithms (AKA Bayesian Decision Theory) to solve this problem, building a framework that allows for iterative refinement of a probabilistic classification state. Because there are no algorithms that fit this problem 'out-of-the-box', we've built our own analysis framework using the emcee and PyMultiNest packages to power the underlying Bayesian inference. I'll give an overview of how our proposed system fits into the wider context of an automated astronomy ecosystem, then give a gentle introduction to Bayesian Decision Theory and how it can be applied to this problem.