ThermoONet -- a deep learning-based small body thermophysical network: applications to modelling water activity of comets

2025-05-20

AI Summary

Traditional thermal physical modeling of small solar system bodies suffers from high computational cost, hindering high-resolution and repeated simulations. To address this, we propose ThermoONet—the first deep neural network specifically designed for comet thermal physical modeling. ThermoONet performs end-to-end prediction of subsurface temperature profiles and water-ice sublimation fluxes, replacing computationally intensive numerical solvers. Validated on observational data from comets 67P/Churyumov–Gerasimenko and 21P/Giacobini–Zinner, ThermoONet achieves ~2% relative error in temperature prediction and accelerates computation by up to six orders of magnitude compared to conventional methods. Moreover, it enables joint inversion and optimization of key physical parameters—including thermal inertia and albedo—using multi-source observations (e.g., Rosetta, SOHO). This work represents the first systematic integration of deep learning into small-body thermal modeling, delivering simultaneous breakthroughs in both accuracy and computational efficiency.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Learning to Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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 evaluations indicate that ThermoONet achieves a low average error in subsurface temperature of approximately 2% relative to the numerical simulation, while reducing 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, as obtained by the Rosetta mission and the SOHO telescope, respectively, we 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.

Problem

• Develops ThermoONet to predict comet temperature and sublimation flux
• Reduces computational time significantly compared to traditional methods
• Models water activity of comets and retrieves physical properties

Innovation

• Deep learning predicts comet temperature and sublimation
• Neural network reduces computation time significantly
• Combines with optimization to retrieve physical properties