Monday 11 November 2019, 3:00pm to 4:00pm
Open toAlumni, Postgraduates, Staff, Undergraduates
RegistrationRegistration not required - just turn up
SuperNNova: Preparing the next decade SN samples with Bayesian Neural Networks
With future surveys such as LSST discovering up to hundred thousand SNe, there is not nearly enough telescope time to spectroscopically confirm each supernova type. Photometric classification methods provide a typing using only the evolution of flux with time, light-curve, for a given supernova. In this talk, I will present SuperNNova, an open source Neural Network framework that is able to obtain photometric samples with purities similar to those of spectroscopically selected samples, <2% contamination, as well to provide a Bayesian interpretation. I will discuss the impact of photometric classifiers in dark energy studies and selection of promising candidates for follow-up, and often neglected pitfalls of machine learning algorithms. I will emphasize the importance of properly calibrated classification probabilities and the additional information that provide epistemic uncertainties when dealing with representativeness issues and out-of-distribution events.