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Human in the loop: adaptive learning in astronomy – Emille E. O. Ishida (Université Clermont Auvergne)
June 19: 12:30 - 13:30
The full exploitation of the next generation of large scale photometric supernova surveys depends heavily on our ability to provide a reliable early-epoch classification based solely on photometric data. In preparation for this scenario, there has been many attempts to apply different machine learning algorithms to the supernova photometric classification problem. Although different methods present different degree of success, text-book machine learning methods fail to address the crucial issue of lack of representativeness between spectroscopic (training) and photometric (target) samples. In this talk I will show how Active Learning (or optimal experiment design) can be used as a tool for optimizing the construction of spectroscopic samples for classification purposes. I will present results on how the design of spectroscopic samples from the beginning of the survey can achieve optimal classification results with a much lower number of spectra than the current adopted strategy.