Kai Polsterer (Heidelberg Institute for Theoretical Studies)

Machine Learning in Astronomy - small lessons learned from learning machines
When Mar 27, 2015 from 10:30 AM to 12:00 PM
  • Colloquium
Where SH Lecture Hall
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The amount and size of astronomical data-sets was growing rapidly in the last decades. Now, with new technologies and dedicated survey telescopes, the databases are growing even faster. VO-standards provide an uniform access to this data. What is still required is a new way to analyze and tools to deal with these large data resources. E.g., common diagnostic diagrams have proven to be good tools to solve questions in the past, but they fail for millions of objects in high dimensional features spaces. By applying technologies from the field of computer sciences this data can be accessed more efficiently. Machine learning is a key tool to make use of the nowadays freely available datasets. This talk exemplarily presents what we learned when using machine learning algorithms on real astronomical data-set.