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colloquia - [ 21.02.2013 (11.00) ]

Machine Learning in Astronomy

Speaker: Dr. Kai Lars Polsterer, Astronomisches Institut, Ruhr-Universität Bochum

In the last decades, the scientific progress in observational astronomy was driven by new kinds of instrumentation and the rapid development in computer science. The former enabled us to observe the sky in new wavelength-ranges with better sensitivity and higher resolution, while the latter provided the tools to process and store the data more efficiently. In recent years and near future, more and more survey projects produce data with rapidly increasing size. Tera-, peta- or even exabyte databases with billions of objects are common sizes. The virtual observatory is an approach to combine all these heterogeneous datasets and to provide a platform to the scientists that allows to access and to analyze them. This talk will present some ideas how machine learning can improve the way of analyzing these datasets. Data-mining techniques are applied to create assistance in the selection of potentially interesting objects for further examination. With the help of so called virtual astronomers, future scientists will be able to train these tools to do their job for a certain task and automatically process billions of sources. Methods to classify or select objects and implementations to solve typical regression problems, even on incomplete datasets, will be discussed. The results of extracting high-redshift quasars from the Sloan digital sky survey will be shown as well as the results of morphological analysis of galaxies. Only by applying new methods of machine learning to the large datasets, scientists with small scale desktop computers can participate in the new era of data-driven astronomy.

Dr. Kai Lars Polsterer - Curriculum vitae

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