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Dynamic Simulation and Parameter Fitting Method of Cometary Dust Based on Machine Learning by Yuxian Yue; Zirui Cao; Haoran Gu; Xiaohui Wang is a Physics and Astronomy article available to read on EtoBox.

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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 groundbased images of Comet 103P/Har

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Author

Yuxian Yue; Zirui Cao; Haoran Gu; Xiaohui Wang

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Physics and Astronomy (Physical Sciences)