학술논문

Cometary surface dust layers built out of millimetre-scale aggregates: dependence of modelled cometary gas production on the layer transport properties
Document Type
Working Paper
Source
Monthly Notices of the Royal Astronomical Society, Volume 522, Issue 3, 2023, Pages 4781-4800
Subject
Astrophysics - Earth and Planetary Astrophysics
Language
Abstract
The standard approach to obtaining knowledge about the properties of the surface layer of a comet from observations of gas production consists of two stages. First, various thermophysical models are used to calculate gas production for a few sets of parameters. Second, a comparison of observations and theoretical predictions is performed. This approach is complicated because the values of many model characteristics are known only approximately. Therefore, it is necessary to investigate the sensitivity of the simulated outgassing to variations in the properties of the surface layer. This problem was recently considered by us for aggregates up to tens of microns in size. For millimetre-size aggregates, a qualitative extension of the method used to model the structural characteristics of the layer is required. It is also necessary to study the role of radiative thermal conductivity, which may play an important role for such large particles. We investigated layers constructed from large aggregates and having various thicknesses and porosity and evaluated the effective sublimation of water ice at different heliocentric distances. For radiative conductivity, approximate commonly used models and the complicated model based on the Dense Medium Radiative Transfer theory were compared. It was shown that for millimetre-size aggregates careful consideration of the radiative thermal conductivity is required since this mechanism of energy transfer may change the resulting gas productivity by several times. We demonstrate that our model is more realistic for an evolved comet than simple models parameterising the properties of the cometary surface layer, yet maintains comparable computational complexity.