Implementing a Neural Network on Forward Models:A Case study for Exoplanet Atmospheres

Simulating exoplanetary atmospheres is essential for describing them, estimating their composition, investigating the presence of haze and clouds, and identifying their relationship with observational signatures. With the emergence of JWST (James Webb Space Telescope) and the upcoming ARIEL (Atmospheric Remotesensing Infrared Exoplanet Large-survey) mission (Tinetti et al. 2018; Pascale, Bezawada, and al. 2018), the need for a fast implementation of the classical forward models is more important than ever. Machine learning (ML) can offer a revolutionary solution to this challenge by providing efficient surrogate models or emulators that approximate the behavior of specific components or the whole system, significantly accelerating the
simulation pipeline.
In our research in general, we are using a 1D self-consistent model including haze/cloud microphysics, disequilibrium chemistry, and radiative transfer interactions to simulate the atmospheric structure of temperate exoplanets from the deep (10³ bar) to the upper thermosphere ( 10⁻¹⁰ bar). This forward model was used in exoplanet studies, like in Arfaux and Lavvas (2022) and many other studies (Lavvas et al. 2019; Arfaux and Lavvas 2023; Lavvas, Paraskevaidou, and Arfaux 2023), offering a more detailed correspondence of the atmospheric composition with the transit observations. In this work we will try to develop a supervised neural network-based surrogate model, with inspiration from Hendrix, Louca, and Miguel (2023), trained on the outputs of the forward model, enabling fast approximation of atmospheric responses for a range of exoplanetary parameters (like planet mass, stellar radius, temperature-pressure profile, stellar flux, metallicity, etc.) without repeated execution of the full model.
We are currently replacing the microphysics of the forward model, which simulates the photochemical haze particle size distribution over a grid of particles radii, with a neural network trained on a given temperature (isothermal), pressure profile, viscosity (which correlates with metallicity), eddy mixing, and gravity. Our goal is to replace the entire forward model (or at least the most time-consuming parts) and improve atmospheric characterization speed and accuracy. This work has the potential to greatly benefit the research community by making comparative studies across planetary systems more accessible to a wider range of groups. It can also be used in hybrid frameworks in which ML handles expensive subcomponents (e.g., radiative transfer) and traditional models handle dynamics, preserving physical interpretability while increasing efficiency.

References
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Arfaux, Anthony and Panayotis Lavvas (Apr. 2023). “A physically derived eddy parametrization for giant planet atmospheres with application on hot-Jupiters”. In: Monthly Notices of the Royal Astronomical Society 522.2, pp. 2525–2542. issn: 0035-8711. doi: 10 . 1093 / mnras / stad1135. eprint: https : / /
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Lavvas, Panayotis, Sofia Paraskevaidou, and Anthony Arfaux (Oct. 2023). “Photochemical hazes clouds in the atmosphere of GJ 1214 b in view of recent JWST observations”. In: 55th Annual Meeting of the Division for Planetary Sciences, id. 223.08. Bulletin of the American Astronomical Society e-id 2023n8i223p08 55.8. url: https://ui.adsabs.harvard.edu/abs/2023DPS....5522308L/abstract.
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