Processing and analysis of large amount of astronomical data on Microsoft Azure HDInsight

S.V. Gerasimov, A.V. Mesheryakov

Abstract


Machine learning provides effective techniques to accurately measure photometric redshifts (photo-z) of extragalactic astronomical objects, which allows researchers to build maps of Large Scale Structure of the Universe. These maps are widely used in various fundamental research fields of extragalactic astrophysics and observational cosmology. Though making predictions by these models for a huge number of objects in astronomical catalogs, containing a broad-band photometry over all the sky, is a challenging task and requires a significant computational resources. In the article we tested the Apache Spark horizontally-scalable framework, deployed in the cloud Microsoft Azure, for the task of photo-z measurements for galaxies from the big photometric dataset of Sloan Digital Sky Survey.

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References


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