Revisiting mass-radius relationships for exoplanet populations : a machine learning insight

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openAccess
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2023-11-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
17
3469-3485
Series
Monthly Notices of the Royal Astronomical Society, Volume 525, issue 3
Abstract
The growing number of exoplanet discoveries and advances in machine learning techniques have opened new avenues for exploring and understanding the characteristics of worlds beyond our Solar system. In this study, we employ efficient machine learning approaches to analyse a data set comprising 762 confirmed exoplanets and eight Solar system planets, aiming to characterize their fundamental quantities. By applying different unsupervised clustering algorithms, we classify the data into two main classes: 'small' and 'giant' planets, with cut-off values at Rp = 8.13R and Mp = 52.48M. This classification reveals an intriguing distinction: giant planets have lower densities, suggesting higher H-He mass fractions, while small planets are denser, composed mainly of heavier elements. We apply various regression models to uncover correlations between physical parameters and their predictive power for exoplanet radius. Our analysis highlights that planetary mass, orbital period, and stellar mass play crucial roles in predicting exoplanet radius. Among the models evaluated, the Support Vector Regression consistently outperforms others, demonstrating its promise for obtaining accurate planetary radius estimates. Furthermore, we derive parametric equations using the M5P and Markov Chain Monte Carlo methods. Notably, our study reveals a noteworthy result: small planets exhibit a positive linear mass-radius relation, aligning with previous findings. Conversely, for giant planets, we observe a strong correlation between planetary radius and the mass of their host stars, which might provide intriguing insights into the relationship between giant planet formation and stellar characteristics.
Description
Publisher Copyright: © 2023 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society.
Keywords
planets and satellites: composition, planets and satellites: dynamical evolution and stability, planets and satellites: formation, planets and satellites: fundamental parameters, planets and satellites: general, software: data analysis
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Citation
Mousavi-Sadr, M, Jassur, D M & Gozaliasl, G 2023, ' Revisiting mass-radius relationships for exoplanet populations : a machine learning insight ', Monthly Notices of the Royal Astronomical Society, vol. 525, no. 3, pp. 3469-3485 . https://doi.org/10.1093/mnras/stad2506