Machine Learning as a Transformative Tool for (Exo-)Planetary Science

TL;DR

This paper reviews how machine learning techniques are revolutionizing planetary and exoplanetary science by addressing complex data analysis challenges such as sequence modeling, pattern recognition, and generative modeling.

Contribution

It highlights innovative ML methodologies developed by NCCR PlanetS members to tackle key challenges in processing heterogeneous planetary datasets.

Findings

ML enables analysis of complex time series data like light curves.

Convolutional neural networks improve feature extraction and pattern recognition.

Generative models facilitate understanding of planetary interior structures.

Abstract

The exploration of planetary bodies in our Solar system and beyond relies on the processing and interpretation of large, spatio-temporally inconsistent, and heterogeneous datasets. Recent advances in machine learning (ML) provide unprecedented opportunities to address many fundamental challenges posed by these heterogeneous and hyper-dimensional datasets. This review chapter highlights innovative ML methodologies that were developed and used by NCCR PlanetS members to address three overarching challenges in (exo)planetary science. The first challenge is sequence modelling, which encompasses the intricate analysis of one-dimensional data such as time series of radial velocities and light curves, among other examples. Secondly, there is pattern recognition that involves studying correlations, leveraging convolutional neural networks for feature extraction, mapping and cross correlation among other examples., anomaly detection through variational autoencoders, and unsupervised clustering of mass spectrometric data. Lastly, there are generative models and emulation-based Bayesian analysis, which encompass the development of predictive models for planetary interior structure, employing Deep Neural Networks to understand planet formation mechanisms. These innovative ML methodologies herald a paradigm shift in the processing of data and numerical models that represent inherent challenges in planetary and exoplanetary science, paving the way for revolutionary discoveries and ideas in this field.