Applied and Computational Engineering
- The Open Access Proceedings Series for Conferences
Proceedings of the 2023 International Conference on Software Engineering and Machine Learning
Series Vol. 8 , 01 August 2023
Open
Access | Article
Identifying Potentially Habitable Exoplanets: A Study using the Transit Method and Kepler dataset
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Abstract
An exoplanet is a planet that orbits a star outside of our solar system. The study of exoplanets is an active area of research in astronomy. In this research, we aim to utilize the Kepler dataset provided by NASA EXOPLANET ACRCHIEVE to identify and classify exoplanets that could potentially support life. The Kepler dataset, which comprises of observations of over 150,000 stars, has been instrumental in the discovery of thousands of exoplanets. We will analyse the dataset using machine learning techniques to classify exoplanets as potentially habitable based on their orbital period, size, distance from their host star, and other parameters. The findings of this research will greatly enhance our understanding of the frequency of life in the universe and the use of machine learning techniques on the Kepler dataset will be an essential tool in the quest for finding potentially habitable exoplanets. Emerging Machine Learning Algorithms aid in detecting habitability of exoplanet in different stellar system. For finding an Exoplanet we have used the “transit method” which is based on the principle that when an exoplanet passes in front of its host star, it causes a temporary dip in the star's brightness. By monitoring the brightness of a star over time, scientists can detect these periodic dips and use them to infer the presence of an exoplanet. The findings of this research have the potential to significantly advance our understanding of the prevalence of life in the universe.
Keywords
exoplanets, Kepler dataset, PHL dataset, machine learning
References
1. Akeson, R. L., et al. "The NASA exoplanet archive: data and tools for exoplanet research."
2. Publications of the Astronomical Society of the Pacific 125.930 (2013): 989
3. J DeVore, John, et al. "On the detection of non-transiting exoplanets with dusty tails." Monthly Notices of the Royal Astronomical Society 461.3 (2016): 2453-2460
4. Cooke, Benjamin F., and Don Pollacco. "specphot: a comparison of spectroscopic and photometric exoplanet follow-up methods." Monthly Notices of the Royal Astronomical Society 495.1 (2020): 734- 742.
5. Basak, Suryoday, et al. "Habitability classification of exoplanets: a machine learning insight." The European Physical Journal Special Topics 230.10 (2021): 2221-2251.
6. Wright, Jason, and Cullen Blake. "Atomically precise sensors could detect a planet much like our own: Earth 2.0: This artist's rendition shows how an Earth-like exoplanet might appear." IEEE Spectrum 58.3 (2021): 22-28.
7. Breitman, Daniela (2017). “How do astronomers find exoplanets?”. EarthSky.org. Online. Retrieved from https://earthsky.org/space/how-do-astronomers-discover-exoplanets/.
8. Santos NC. Extra-solar planets: Detection methods and results. New Astronomy Reviews. 2008 Jun 1;52(2-5):154-66.
9. Brownlee, Jason. "Discover feature engineering, how to engineer features and how to get good at it." Machine Learning Process (2014)
10. Mishra, Rajeev. "Predicting habitable exoplanets from NASA's Kepler mission data using Machine Learning." (2017).
11. Moulds, V. E., et al. "Finding exoplanets orbiting young active stars–I. Technique." Monthly Notices of the Royal Astronomical Society 430.3 (2013): 1709-1721.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2023 International Conference on Software Engineering and Machine Learning
- ISBN (Print)
- 978-1-915371-63-8
- ISBN (Online)
- 978-1-915371-64-5
- Published Date
- 01 August 2023
- Series
- Applied and Computational Engineering
- ISSN (Print)
- 2755-2721
- ISSN (Online)
- 2755-273X
- DOI
- 10.54254/2755-2721/8/20230089
- Copyright
- 01 August 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited