The interpretation of the origin of observed exoplanets is usually done only qualitatively due to uncertainties of key parameters in planet formation models.
Neural Network
Grid-based Exoplanet Atmospheric Mass Loss Predictions Through Neural Network
The fast and accurate estimation of planetary mass-loss rates is critical for planet population and evolution modelling.
Neural Network Constraints on the Cosmic-Ray Ionization Rate and Other Physical Conditions in NGC 253 with ALCHEMI Measurements of HCN and HNC
We use a neural network model and ALMA observations of HCN and HNC to constrain the physical conditions, most notably the cosmic-ray ionization rate (CRIR, zeta), in the Central Molecular […]
AI in Space for Scientific Missions: Strategies for Minimizing Neural-Network Model Upload
Artificial Intelligence (AI) has the potential to revolutionize space exploration by delegating several spacecraft decisions to an onboard AI instead of relying on ground control and predefined procedures.
Using A Neural Network Approach To Accelerate Disequilibrium Chemistry Calculations In Exoplanet Atmospheres
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 […]
Neural Networks: Solving The Chemistry Of The Interstellar Medium
Non-equilibrium chemistry is a key process in the study of the InterStellar Medium (ISM), in particular the formation of molecular clouds and thus stars.
