Machine Learning Approaches for Predicting Exoplanet Livability: A Comprehensive Analysis

DOI

10.2991/978-94-6463-370-2_69How to use a DOI?

Keywords

Linear Regression; Planet Livability Rate; Machine Learning Prediction

Abstract

Now, the continuous advancement of astronomical observation equipment has led to a steady rise in the quantity of identified exoplanets. Hence, there is a pressing need for the development of an efficient and practical approach to forecasting the livability indices of these exoplanets. The objective of this study is to provide a comprehensive overview and evaluation of the methodologies employed in predicting the livability quotient of exoplanets through the utilization of machine learning techniques. This research endeavor holds the potential to enhance astronomers’ capacity to discern habitable exoplanets with more accuracy and precision. This study does preliminary data processing on the observations obtained from Kepler’s astronomical telescope. Subsequently, via an examination of the fundamental characteristics of exoplanets, this study puts forth many theoretical frameworks, ultimately employing a linear regression model to ascertain analogous functional associations among variables. In the paragraph on the experiment and application, this paper describes the whole experiment and its results. Some graphs with data are also added. At the end of this review, the author summarizes the research results, including the summarization of the methods and experimental results of using machine learning to predict the evaluation rate of planetary habitability. This paper also gives ideas about the deployment and application of this research.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Siwei Wang
PY  - 2024
DA  - 2024/02/14
TI  - Machine Learning Approaches for Predicting Exoplanet Livability: A Comprehensive Analysis
BT  - Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)
PB  - Atlantis Press
SP  - 677
EP  - 694
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-370-2_69
DO  - 10.2991/978-94-6463-370-2_69
ID  - Wang2024
ER  -