Kepler Light Curve Classification Using Deep Learning and Markov Transition Field (Student Abstract)
Abstract
An exoplanet is a planet, which is not a part of our solar system. Whether life exists in one or more of these exoplanets has fascinated humans for centuries. NASA’s Kepler Space Telescope has discovered more than 70% of known exoplanets in our universe. However, manually determining whether a Kepler light curve indicates an exoplanet or not becomes infeasible with the large volume of data. Due to this, we propose a deep learning-based strategy to automatically classify a Kepler light curve. More specifically, we first convert the light curve time series into its corresponding Markov Transition Field (MTF) image and then classify it. Results show that the accuracy of the proposed technique is 99.39%, which is higher than all current state-of-the-art approaches.
Cite
Text
Donnelly and Dutta. "Kepler Light Curve Classification Using Deep Learning and Markov Transition Field (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30435Markdown
[Donnelly and Dutta. "Kepler Light Curve Classification Using Deep Learning and Markov Transition Field (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/donnelly2024aaai-kepler/) doi:10.1609/AAAI.V38I21.30435BibTeX
@inproceedings{donnelly2024aaai-kepler,
title = {{Kepler Light Curve Classification Using Deep Learning and Markov Transition Field (Student Abstract)}},
author = {Donnelly, Shane and Dutta, Ayan},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {23475-23476},
doi = {10.1609/AAAI.V38I21.30435},
url = {https://mlanthology.org/aaai/2024/donnelly2024aaai-kepler/}
}