Analysis of selected algorithms for the classification of space objects?

Abstract

Radoslaw Jedrzejczyk* and Katarzyna Kleczek

Along with the rise of available astronomical data, captured from numerous facilities from around the world, a need for faster and more sophisticated data analysis methods emerges. Data captures from numerous observation of large quantities of object in the sky can reach large volumes very quickly, making it impossible for scientist to analyze by hand. This rises the need for fast and reliable automated methods of data processing, which can be found in computer science research. Leveraging algorithms used in different areas of research is crucial for processing information about celestial bodies. In this work, we apply machine learning methods from computer science domain into an astronomy problem. We lay out three different machine learning algorithms, along with their inner workings, and show how they can be applied to astronomy problems. We show how those algorithms can be used to speed up processing of large volumes of data, and how they can help scientists in classification of celestial bodies. We investigate how each algorithm performs and try to find the best performing one in the problem of classification of different objects, based on their characteristics.

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