Using a neural network approach to accelerate disequilibrium chemistry calculations in exoplanet atmospheres
arXiv:2306.07074 · doi:10.1093/mnras/stad1763
Abstract
In this era of exoplanet characterisation with JWST, the need for a fast implementation of classical forward models to understand the chemical and physical processes in exoplanet atmospheres is more important than ever. Notably, the time-dependent ordinary differential equations to be solved by chemical kinetics codes are very time-consuming to compute. In this study, we focus on the implementation of neural networks to replace mathematical frameworks in one-dimensional chemical kinetics codes. Using the gravity profile, temperature-pressure profiles, initial mixing ratios, and stellar flux of a sample of hot-Jupiters atmospheres as free parameters, the neural network is built to predict the mixing ratio outputs in steady state. The architecture of the network is composed of individual autoencoders for each input variable to reduce the input dimensionality, which is then used as the input training data for an LSTM-like neural network. Results show that the autoencoders for the mixing ratios, stellar spectra, and pressure profiles are exceedingly successful in encoding and decoding the data. Our results show that in 90% of the cases, the fully trained model is able to predict the evolved mixing ratios of the species in the hot-Jupiter atmosphere simulations. The fully trained model is ~1000 times faster than the simulations done with the forward, chemical kinetics model while making accurate predictions.
arXiv comment13 pages, 9 figures, accepted for publication at MNRAS
References in corpus (31)
- Probabilistic Forecasting of the Masses and Radii of Other Worlds
- Disequilibrium Carbon, Oxygen, and Nitrogen Chemistry in the Atmospheres of HD 189733b and HD 209458b
- The MUSCLES Treasury Survey I: Motivation and Overview
- Identifying Exoplanets with Deep Learning: A Five Planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90
- Early Release Science of the exoplanet WASP-39b with JWST NIRSpec PRISM
- Photochemistry in Terrestrial Exoplanet Atmospheres I: Photochemistry Model and Benchmark Cases
- Early Release Science of the Exoplanet WASP-39b with JWST NIRSpec G395H
- Chromospheric Activity, Rotation, and Rotational Braking in M and L Dwarfs
- Equilibrium chemistry down to 100 K - Impact of silicates and phyllosilicates on carbon/oxygen ratio
- Photochemically-produced SO in the atmosphere of WASP-39b
- A chemical model for the atmosphere of hot Jupiters
- The MUSCLES Treasury Survey II: Intrinsic Lyman Alpha and Extreme Ultraviolet Spectra of K and M Dwarfs with Exoplanets
- Dynamics and Disequilibrium Carbon Chemistry in Hot Jupiter Atmospheres, With Application to HD 209458b
- Early Release Science of the exoplanet WASP-39b with JWST NIRISS
- The MUSCLES Treasury Survey III: X-ray to Infrared Spectra of 11 M and K Stars Hosting Planets
- VULCAN: an Open-Source, Validated Chemical Kinetics Python Code for Exoplanetary Atmospheres
- Early Release Science of the exoplanet WASP-39b with JWST NIRCam
- FastChem: A computer program for efficient complex chemical equilibrium calculations in the neutral/ionized gas phase with applications to stellar and planetary atmospheres
- Analytical Models of Exoplanetary Atmospheres. II. Radiative Transfer via the Two-stream Approximation
- Exploring atmospheres of hot mini-Neptunes and extrasolar giant planets orbiting different stars with application to HD 97658b, WASP-12b, CoRoT-2b, XO-1b and HD 189733b
- Identifying Exoplanets with Deep Learning II: Two New Super-Earths Uncovered by a Neural Network in K2 Data
- An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
- Evidence for disequilibrium chemistry from vertical mixing in hot Jupiter atmospheres. A comprehensive survey of transiting close-in gas giant exoplanets with warm-Spitzer/IRAC
- Implementation of disequilibrium chemistry to spectral retrieval code ARCiS and application to sixteen exoplanet transmission spectra: Indication of disequilibrium chemistry for HD 209458b and WASP-39b
- On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars
- Assessment of Supervised Machine Learning for Atmospheric Retrieval of Exoplanets
- A chemical kinetics code for modelling exoplanetary atmospheres
- Reducing the complexity of chemical networks via interpretable autoencoders
- LSTM and CNN application for core-collapse supernova search in gravitational wave real data
- Chemulator: Fast, accurate thermochemistry for dynamical models through emulation
- Machine learning-accelerated chemistry modeling of protoplanetary disks