ThermoONet: Deep learning-based small-body thermophysical network
Year: 2025
Type: article
Abstract: Cometary activity is a compelling subject of study, with thermophysical models playing a pivotal role in its understanding. However, traditional numerical solutions for small body thermophysical models are computationally intensive, posing challenges for investigations requiring high-resolution or repetitive modeling. To address this limitation, we employed a machine learning approach to develop ThermoONet – a neural network designed to predict the temperature and water ice sublimation flux of comets. Performance évaluations indicate that ThermoONet achieves a low average error in subsurface temperature of approximately 2% relative to the numerical simulation, while reducing the computational time by nearly six orders of magnitude. We applied ThermoONet to model the water activity of comets 67P/Churyumov-Gerasimenko and 21P/Giacobini-Zinner. By successfully fitting the water production rate curves of these comets, obtained by the Rosetta mission and the SOHO telescope, respectively, we have been able to demonstrate the network's effectiveness and efficiency. Furthermore, when combined with a global optimization algorithm, ThermoONet proves capable of retrieving the physical properties of target bodies.
Source: Astronomy and Astrophysics
Authors: Shunjing Zhao, Xian Shi, Hanlun Lei
Language: English
Topic: Astro and Planetary Science
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $1,865
Awards: G2787048292, G3307874106
OpenAlex ID: W4410140514