Classification of Extragalactic X-ray Sources Using Machine Learning Method




Rostami Osanloo, Mehrdad

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Only a small fraction of extragalactic X-ray sources have reliable classifications. Although a large amount of X-ray data exists in the archives, the X-ray data alone are not enough to reveal the nature of the X-ray sources, and multi-wavelength data is the only way to make progress toward this goal. Therefore, creating an automated Machine Learning (ML) tool for classification of extragalactic X-ray sources with multi-wavelength data will enable us to understand X-ray source populations in a plethora of nearby galaxies. Modern ML methods can be used to quickly analyze the vast amount of multi-wavelength data for these unclassified sources providing both the classifications and their confidences. To this end, we have created a ML pipeline to classify extragalactic X-ray sources, which can utilize the large amount of existing archive data taken with Hubble Space Telescope. The use of the Hubble Space Telescope is essential when dealing with extragalacic sources, and we have adopted our pipeline accordingly. The tool that we have developed will open new avenues to explore extragalactic astronomy.



Random forest classification, X-ray astronomy, Extragalactic x-ray sources machine learning


Rostami Osanloo, M. (2019). <i>Classification of extragalactic x-ray sources using machine learning method</i> (Unpublished thesis). Texas State University, San Marcos, Texas.


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