Grid-based exoplanet atmospheric mass loss predictions through neural network

TL;DR

This paper introduces MLink, a neural network-based interpolation method that accurately estimates planetary atmospheric mass-loss rates from large hydrodynamic model grids, outperforming classical schemes and aiding planetary evolution studies.

Contribution

The paper presents a novel neural network interpolation scheme for planetary mass-loss rates, significantly improving accuracy over traditional methods and enabling efficient analysis of large model grids.

Findings

MLink reduces interpolation errors compared to classical methods.

Evolutionary tracks with MLink are comparable to classical schemes at Gyr timescales.

Differences near the radius gap can exceed observational uncertainties.

Abstract

The fast and accurate estimation of planetary mass-loss rates is critical for planet population and evolution modelling. We use machine learning (ML) for fast interpolation across an existing large grid of hydrodynamic upper atmosphere models, providing mass-loss rates for any planet inside the grid boundaries with superior accuracy compared to previously published interpolation schemes. We consider an already available grid comprising about 11000 hydrodynamic upper atmosphere models for training and generate an additional grid of about 250 models for testing purposes. We develop the ML interpolation scheme (dubbed "atmospheric Mass Loss INquiry frameworK"; MLink) using a Dense Neural Network, further comparing the results with what was obtained employing classical approaches (e.g. linear interpolation and radial basis function-based regression). Finally, we study the impact of the different interpolation schemes on the evolution of a small sample of carefully selected synthetic planets. MLink provides high-quality interpolation across the entire parameter space by significantly reducing both the number of points with large interpolation errors and the maximum interpolation error compared to previously available schemes. For most cases, evolutionary tracks computed employing MLink and classical schemes lead to comparable planetary parameters at Gyr-timescales. However, particularly for planets close to the top edge of the radius gap, the difference between the predicted planetary radii at a given age of tracks obtained employing MLink and classical interpolation schemes can exceed the typical observational uncertainties. Machine learning can be successfully used to estimate atmospheric mass-loss rates from model grids paving the way to explore future larger and more complex grids of models computed accounting for more physical processes.