ThermoONet -- Deep Learning-based Small Body Thermophysical Network

Understanding the surface and subsurface temperature distributions of small bodies in the Solar System is fundamental to thermophysical studies, which provide insight into their composition, evolution, and dynamical behavior [1,2]. Thermophysical models are essential tools for this purpose, but conventional numerical treatments are often computationally expensive. This limitation presents significant challenges, particularly for studies requiring high-resolution simulations or large-scale, repeated calculations across parameter spaces.

To overcome these computational bottlenecks, we developed ThermoONet -- a deep learning-based neural network designed to efficiently and accurately predict temperature distributions for small Solar System bodies [3,4]. ThermoONet is trained on results from traditional thermophysical simulations and is capable of replicating their accuracy with dramatically reduced computational cost. We apply ThermoONet to two representative cases: modeling the surface temperature of asteroids and the subsurface temperature of comets. Evaluation against numerical benchmarks shows that ThermoONet achieves mean relative errors of approximately 1% for asteroids and 2% for comets, while reducing computation time by over five orders of magnitude.

We test the ability of ThermoONet with two scientifically compelling yet computationally heavy tasks. We model the long-term orbit evolution of asteroids (3200) Phaethon and (89433) 2001 WM41 using N-body simulations augmented by instantaneous Yarkovsky accelerations derived from ThermoONet-driven thermophysical modelling [3]. Results show that by applying ThermoONet, it is possible to employ actual shapes of asteroids for high-fidelity modelling of the Yarkovsky effect. Furthermore, we employ ThermoONet to simulate water ice activity of comets [4]. By fitting the water production rate curves of comets 67P/Churyumov-Gerasimenko and 21P/Giacobini-Zinner, we show that ThermoONet could be of use for the inversion of physical properties of comets that are difficult to achieve with traditional methods.

[1] Delbo, M., Mueller, M., Emery, J.P., Rozitis, B. and Capria, M.T., 2015. Asteroid thermophysical modeling. Asteroids iv, 1, pp.107-128.

[2] Prialnik, D., Benkhoff, J. and Podolak, M., 2004. Modeling the structure and activity of comet nuclei. Comets II, 1, pp.359-387.

[3] Zhao, S., Lei, H. and Shi, X., 2024. Deep operator neural network applied to efficient computation of asteroid surface temperature and the Yarkovsky effect. Astronomy & Astrophysics, 691, p.A224.

[4] Zhao, S., Shi, X. and Lei, H., 2025. ThermoONet: Deep learning-based small-body thermophysical network: Applications to modeling the water activity of comets. Astronomy & Astrophysics, in press.