Exoplanetology: Exoplanets & Exomoons

DARWEN: Data-driven Algorithm for Reduction of Wide Exoplanetary Networks

By Keith Cowing

Status Report

astro-ph.EP

December 11, 2024

Filed under astro-ph.EP, astrochemistry, atmosphere, data, ELT, exoplanet, global circulation model, JWST, modeling, Photochemistry

DARWEN: Data-driven Algorithm for Reduction of Wide Exoplanetary Networks — Validation and low-cost schemes’ performance on key and major molecules. The plots illustrate the performance of our method on key molecules for HD 209458b (a) and HD 189733b (b). Left: Molar fractions of key molecules as a function of pressure in the atmosphere. The full V20 model is represented by a solid line, DARWEN’s validation scheme by a dashed-dotted line, low-cost schemes by a dashed line, and the reduced scheme (R20) by a dotted line. Right: Maximal percentage changes in the molar fractions of the key molecules compared to the full model. The full model’s line is not shown because it serves as the reference model. Lines close to zero indicate that the predictions are nearly identical to those of the full model for that molecule. We only show changes bigger than 1%. — astro-ph.EP

Exoplanet atmospheric modeling is advancing from chemically diverse one-dimensional (1D) models to three-dimensional (3D) global circulation models (GCMs), which are crucial for interpreting observations from facilities like the James Webb Space Telescope (JWST) and Extremely Large Telescope (ELT).

However, maintaining chemical diversity in models, especially in GCMs, is computationally expensive, limiting their complexity. Optimizing the number of reactions and species can address this tradeoff, but transparent and efficient methods for such optimization are lacking in current exoplanet literature.

We aim to develop a systematic approach for reducing chemical networks in exoplanetary atmospheres while balancing accuracy and computational efficiency. Our data-driven method selects optimal reduced chemical networks based on accuracy and computational efficiency metrics. This approach can optimize networks for similar planets simultaneously, assign weights to prioritize accuracy or efficiency, and is applicable when including photochemistry.

We base our method on sensitivity analysis of a typical 1D chemical kinetics model, applying principal component analysis to the sensitivities. To achieve fast and reliable network reduction, we utilize a genetic algorithm, a machine-learning optimization method that mimics natural selection. We present three schemes tailored for different priorities (accuracy, computational efficiency, and adaptability to photochemistry) that demonstrate improved performance and reduced computational costs.

Our genetic algorithm-based method, the first to reduce a chemical network including photochemistry in exoplanet research, offers a versatile and efficient approach to enhance both accuracy and computational efficiency.

A. Lira-Barria, J. N. Harvey, T. Konings, R. Baeyens, C. Henríquez, L. Decin, O. Venot, R. Veillet

Subjects: Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM)
Cite as: arXiv:2412.04359 [astro-ph.EP] (or arXiv:2412.04359v1 [astro-ph.EP] for this version)
https://doi.org/10.48550/arXiv.2412.04359
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Related DOI:
https://doi.org/10.1051/0004-6361/202452070
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Submission history
From: Arturo Lira-Barria
[v1] Thu, 5 Dec 2024 17:18:45 UTC (1,048 KB)
https://arxiv.org/abs/2412.04359
Astrobiology

Keith Cowing

Explorers Club Fellow, ex-NASA Space Station Payload manager/space biologist, Away Teams, Journalist, Lapsed climber, Synaesthete, Na’Vi-Jedi-Freman-Buddhist-mix, ASL, Devon Island and Everest Base Camp veteran, (he/him) 🖖🏻

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