https://doi.org/10.1140/epjs/s11734-021-00203-z
Regular Article
Habitability classification of exoplanets: a machine learning insight
1
Department of Computer Science and Engineering, Pennsylvania State University, 16801, State College, PA, USA
2
Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Bengaluru, India
3
Microsoft India Private limited, Bengaluru, India
4
Department of Computer Science, 602 ICT Building, University of Calgary, 2500 University Drive NW, T2N 1N4, Calgary AB, Canada
5
Indian Institute of Astrophysics, Sarjapur Main Road, 2nd Block, Koramangala, 560034, Bengaluru, Karnataka, India
Received:
18
July
2020
Accepted:
23
June
2021
Published online:
21
July
2021
We explore the efficacy of machine learning (ML) in characterizing exoplanets into different classes. The source of the data used in this work is University of Puerto Rico’s Planetary Habitability Laboratory’s Exoplanets Catalog (PHL-EC). We perform a detailed analysis of the structure of the data and propose methods that can be used to effectively categorize new exoplanet samples. Our contributions are twofold. We elaborate on the results obtained by using ML algorithms by stating the accuracy of each method used and propose a paradigm to automate the task of exoplanet classification for relevant outcomes. In particular, we focus on the results obtained by novel neural network architectures for the classification task, as they have performed very well despite complexities that are inherent to this problem. The exploration led to the development of new methods fundamental and relevant to the context of the problem and beyond. The data exploration and experimentation also result in the development of a general data methodology and a set of best practices which can be used for exploratory data analysis experiments.
© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2021
