Machine Learning for Classification Strategies for Astronomical Sources
Dr. Nina Hernitschek
Postdoctoral Fellow, Data Science Institute & Department of Physics and 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. Various techniques for extracting information, such as structure function fitting and template-based period fitting, are applied before a subsequent machine-learning classification searches for and classifies variable sources. 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 to apply specific methods to typical problems occurring from large time-domain surveys such as Pan-STARRS1, ZTF and finally LSST.
All interested persons are invited to attend remotely—email email@example.com for information.
Originally published at physics.nd.edu.