Machine Learning Revolutionizing (Exo-)Planetary Science Data Analysis

This review highlights the transformative role of machine learning (ML) in addressing fundamental challenges in (exo)planetary science. The exploration of planetary bodies generates vast, spatio-temporally inconsistent, and heterogeneous datasets, which are often difficult to analyze using traditional methods. ML provides unprecedented opportunities to extract meaningful insights from these complex datasets. The review focuses on three key areas where ML is making a significant impact: sequence modeling, pattern recognition, and generative models. Sequence modeling enables the analysis of one-dimensional data such as time series of radial velocities and light curves. Pattern recognition leverages convolutional neural networks for feature extraction and anomaly detection. Generative models and emulation-based Bayesian analysis are used for predictive modeling of planetary interior structure and planet formation mechanisms. These innovative ML methodologies represent a paradigm shift in the processing of data and numerical models in planetary and exoplanetary science. By enabling scientists to analyze data more efficiently and effectively, ML is paving the way for revolutionary discoveries and a deeper understanding of the universe.

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