Data Science in Astronomy
January 31, 2020 | 12pm
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Data Science In Astronomy
In the era of large-scale astronomical surveys, methods to investigate these data, and especially to classify astronomical objects, become more and more important. I will give a brief overview of state-of-the-art methods of data handling and machine learning techniques used in astronomy, as well as in more detail describe how I apply specific methods to typical problems occuring from large time-domain surveys such as Pan-STARRS1 3pi survey. As the survey strategy of Pan-STARRS1 3pi led to sparse, non-simultaneous lightcurves, this is also a testbed to explore the possibilities in investigate the sparse data that will occur during the first months or years of upcoming large-scale surveys when only a fraction of the total observing is done.
Nina Hernitschek received her B.Sc. and M.Sc. degrees in physics from University of Heidelberg, Germany, and her Ph.D. in astrophysics from International Max Planck Research School for Astronomy/ University of Heidelberg in spring 2017. After three years as a postdoc in the Department of Astronomy at Caltech, she is currently a postdoctoral fellow with Vanderbilt University’s Data Science Institute and the Department of Physics and Astronomy. Her current research interests include machine learning methods for detection and classification of variable sources in large and deep all-sky survey. She is interested a lot in the dynamical history of our Milky Way – especially halo formation and the disruption of globular clusters and satellites – , which can tell us a lot about galaxy evolution in general.