2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

Palestri, P.; Caruso, Eva M.; Driussi, F.; Esseni, D.; Lizzit, Daniel; Osgnach, P.; Venica, Stefano; Mikac, L.; Ivanda, Mile; Gotić, Marijan; Ristić, Dušan; Djerek, V.; Gebavi, Hrvoje; Trusso, S.; D’Andrea, Chiara

doi:10.1109/mipro35064.2015

2015

DOI: 10.1109/mipro35064.2015

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2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

P. Palestri¹,

Eva M. Caruso²,

F. Driussi³

et al.

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2023

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

Mousavi-Sadr,

Jassur,

Gozaliasl

2023

Monthly Notices of the Royal Astronomical Society

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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.

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

Mousavi-Sadr,

Jassur,

Gozaliasl

2023

Monthly Notices of the Royal Astronomical Society

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show abstract

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2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

Cited by 1 publication

References 12 publications

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

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

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