A machine learning approach to meteor light curve analysis

In this work we conduct a thorough examination of utilizing machine learning and computervision techniques for classifying meteors based on their characteristics. The focus of the re-search is the analysis of light curves emitted by meteors as they pass through the Earth’s atmo-sphere, including aspects such as luminosity, duration, and shape. Through extracting featuresfrom these light curves and comparing them to established meteors orbits, valuable informa-tion about the meteor’s origin and chemical composition is sought to be obtained. A significantcontribution of the thesis is the development of methods for classifying meteors by extractingfeatures from the light curve shape through the usage of unsupervised classification algorithms.This approach allows for the automatic classification of meteors into various groups based ontheir properties. Data for the research is collected by a three-camera setup at the Armagh observatory,comprising one medium-angle camera andtwo wide-angle cameras. This setup enables the capturing of detailed images of meteor lightcurves, as well as various other observations such as coordinate and angular data. The researchalso involves the use of machine learning algorithms for data reduction and classification tasks.By applying these techniques to the data collected from the camera setup, the identification ofparent objects based on chemical composition and meteor path is facilitated, along with theacquisition of other valuable information about the meteors.