Home / Current Issue / Paper 1714036
Exoplanet Detection Using Machine Learning
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
DOI: https://doi.org/10.64388/IREV9I8-1714036
Abstract
The detection of exoplanets plays a crucial role in understanding planetary systems beyond our solar system. Traditional detection techniques often require extensive manual verification, making automated solutions desirable. In this study, a machine learning–based approach for exoplanet detection is proposed using data from the NASA Kepler mission. After preprocessing and feature selection, a Random Forest classifier is trained to distinguish confirmed exoplanets from false positives. Experimental results demonstrate that the proposed model achieves an accuracy of 99.29
References
[1] W. J. Borucki, D. Koch, G. Basri, N. Batalha, T. Brown, and D. Caldwell, “Kepler planet- detection mission: Introduction and first results,” Science, vol. 327, no. 5968, pp. 977–980, Feb. 2010.
[2] J. L. Jenkins, D. A. Caldwell, H. Chandrasekaran, J. D. Twicken, S. Seader, and J. A. Carter, “Overview of the Kepler science processing pipeline,” Astrophysical Journal Letters, vol. 713, no. 2, pp. L87–L91, Apr. 2010.
[3] A. Vanderburg and J. A. Johnson, “A technique for extracting highly precise photometry for the two-wheeled Kepler mission,” Publications of the Astronomical Society of the Pacific, vol. 126, no. 944, pp. 948–958, Oct. 2014.
[4] L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, Oct. 2001.
[5] S. McCauliff, J. Jenkins, E. Catanzarite, J. Burke, and J. Twicken, “Automatic classification of Kepler planet candidates,” Astrophysical Journal, vol. 806, no. 1, pp. 1–15, June 2015.
[6] R. R. Kumar, S. K. Sahoo, and A. K. Rath, “Exoplanet detection using machine learning techniques,” International Journal of Engineering and Advanced Technology, vol. 8, no. 5, pp. 1134–1139, May 2019.
[7] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. New York, NY, USA: Springer, 2009.
[8] C. J. Burke, J. L. Christiansen, J. L. Mullally, J. F. Rowe, and T. S. Barclay, “Terrestrial planet occurrence rates for the Kepler GK dwarf sample,” Astrophysical Journal, vol. 809, no. 1, pp. 1–22, Aug. 2015.
[9] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, and V. Dubourg, “Scikit- learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825– 2830, Oct. 2011.
[10] NASA Exoplanet Science Institute, “NASA Exoplanet Archive: Kepler data products,” California Institute of Technology, Pasadena, CA, USA, 2023.
[11] J. R. Thompson, T. D. Morton, and E. Petigura, “A machine learning technique for automated vetting of Kepler transit signals,” Astronomical Journal, vol. 161, no. 3, pp. 1–14, Mar. 2021.
[12] A. Pearson, R. P. Butler, and S. Vogt, “Photometric noise sources in space-based exoplanet surveys,” Astronomy and Astrophysics, vol. 610, pp. A12–A20, Feb. 2018.
How to cite this paper
@article{1714036,
author = {Saachi Sawant},
title = {Exoplanet Detection Using Machine Learning},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {8},
pages = {87-93},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1714036.pdf},
abstract = {The detection of exoplanets plays a crucial role in understanding planetary systems beyond our solar system. Traditional detection techniques often require extensive manual verification, making automated solutions desirable. In this study, a machine learning–based approach for exoplanet detection is proposed using data from the NASA Kepler mission. After preprocessing and feature selection, a Random Forest classifier is trained to distinguish confirmed exoplanets from false positives. Experimental results demonstrate that the proposed model achieves an accuracy of 99.29},
month = {February},
doi = {https://doi.org/10.64388/IREV9I8-1714036}
}