Dynamic simulation and parameter fitting method of cometary dust based on machine learning

Cometary dust is refractory particles lifted and ejected by sublimation of volatiles from the surface of comets with typical diameters from sub-micrometers to centimeters. These particles distribute around and recede from cometary nuclei and are illuminated by the Sun, forming the observable dust comae and tails. The fundamental characteristics of cometary dust, such as their size and size distribution, ejection velocity from the nuclei, etc., are of great significance to understanding the formation and evolution of comets. This paper presents a method, the Machine Learning Based Dynamic Parameter Fitting method (MLDPF method), for deriving the fundamental properties of cometary dust based on dynamical simulation and machine learning. In this process, the Monte Carlo method is used to generate dust particles in the assumed parameter space and solve for the spatial distributions of dust at the times of observations according to dynamical models. Then, we use these simulations to train a CNN (Convolutional Neural Networks) model, and finally fit the observed photos to derive the parameters of the cometary dust. Using this approach, we analyzed the ground-based images of Comet 103P/Hartley collected in the visible-band. The dust ejected from 103P is dominated by micro-particle is 2.11 ± 0.41 μm in radius, and follows an assumed exponential dust size distribution with a coefficient of −4.26 ± 1.41. The initial ejection velocity of dust is 87 ± 13.4 m/s. The dust producing rate is about 11.35 × 1012 s−1 according to the Afρ parameter obtained from the optical photos and the best-fit parameters. The dust emission is likely to be solar insolation dependent. The machine learning and the fitting process were able to converge to a set of solutions that are in good agreement with the previous analyses in the literature. The MLDPF method can establish a cometary dust parameter-image model by machine learning, in which training and test set of the model were calculated based on simulated images; and then the model can be applied to real images. It has a wide range of applicability to different comets and can be used to predict comet morphology. This method also has the scalability of parameters and can be used for the study of cometary dust under more complex parameters and dynamics.