EGU24-21463, updated on 11 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

A machine learning approach to meteor light curve analysis

Lucas Mandl1,3, Apostolous Christou2, and Andreas Windisch1,3,4
Lucas Mandl et al.
  • 1University of Applied Sciences FH JOANNEUM, Graz, Austria
  • 2Armagh Observatory & Planetarium, Armagh, UK
  • 3JOANNEUM RESEARCH Forschungsgesellschaft mbH, Graz, Austria
  • 4Physics Department, Washington Universirty in St. Louis, St. Louis, MO, USA

In this work we conduct a thorough examination of utilizing machine learning and computer
vision 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 features
from 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 significant
contribution of the thesis is the development of methods for classifying meteors by extracting
features 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 on
their properties. Data for the research is collected by a three-camera setup at the Armagh observatory,
comprising one medium-angle camera and
two wide-angle cameras. This setup enables the capturing of detailed images of meteor light
curves, as well as various other observations such as coordinate and angular data. The research
also 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 of
parent objects based on chemical composition and meteor path is facilitated, along with the
acquisition of other valuable information about the meteors.

How to cite: Mandl, L., Christou, A., and Windisch, A.: A machine learning approach to meteor light curve analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21463,, 2024.