Thursday 08 Oct 2015Modelling Light Curves for Improved Classification of Astronomical Objects

Professor Julian Faraway - Bath University

H101 14:30-16:30


Many synoptic surveys are observing large parts of the sky multiple

times.  The resulting lightcurves provide a window to the

dynamic nature of the universe.  However, there are many significant

challenges in analyzing these lightcurves.  We describe a

modeling-based approach using Gaussian process regression for

generating critical measures for the classification of such

lightcurves. This method has key advantages over other popular

nonparametric regression methods in its ability to deal with

censoring, a mixture of sparsely and densely sampled curves, the

presence of annual gaps caused by objects not being visible

throughout the year from a given position on Earth and known but

variable measurement errors. 


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