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Machine Learning & Supernovae

8 Aug 2012, 11:39 UTC
Machine Learning & Supernovae
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This post, from Berkley statistician Joey Richards, is one of three marking the end of this phase of the Galaxy Zoo : Supernova project. You can hear from project lead Mark Sullivan here, and from the Zooniverse’s Chris Lintott here.
Thanks to the efforts of the Galaxy Zoo Supernovae community, researchers in the Palomar Transient Factory collaboration have constructed a machine-learned (ML) classifier that can reliably predict, in near real-time, whether each candidate is a real supernova. ML classification operates by employing previously vetted data to teach computer algorithms a statistical model that can accurately and automatically predict the class for each new candidate (i.e., real transient or not) from observed data on that object. The manual vetting of tens of thousands of supernova candidates by the Galaxy Zoo community has provided PTF an invaluable data set which could be used to accurately train such a ML classifier.
The ML approach is appealing for supernova vetting because it allows us to make probabilistic classification statements, in real-time, about the validity of each new candidate. Further, it allows the simultaneous use of many data sources, including both new and reference PTF imaging data, historical PTF light curves, and information from external, ...

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