Laboratory-based grain-shape models for simulating dust infrared spectra

TL;DR

This study evaluates a statistical light-scattering model with a distribution of form factors to accurately reproduce laboratory-measured infrared dust spectra, accounting for grain shape effects and anisotropy.

Contribution

It introduces a method to derive grain shape information from infrared spectra using the DFF model and fitting algorithms, improving dust mineralogy analysis.

Findings

Irregular particles are best fitted with a Gaussian random sphere DFF.

Roundish grains match fractal aggregate DFFs with specific fractal dimensions.

Different DFFs are required for different crystallographic axes in anisotropic materials.

Abstract

Analysis of thermal dust emission spectra for dust mineralogy and physical grain properties depends on laboratory-measured or calculated comparison spectra. Often, the agreement between these two kinds of spectra is not satisfactory because of the strong influence of the grain morphology on the spectra. We investigate the ability of the statistical light-scattering model with a distribution of form factors (DFF model) to reproduce experimentally measured infrared extinction spectra for particles that are small compared to the wavelength. We take advantage of new experimental spectra measured for free particles dispersed in air with accompanying information on the grain morphology. For the calculations, we used DFFs that were derived for aggregates of spherical grains, as well as for compact grain shapes corresponding to Gaussian random spheres. Irregular particle shapes require a DFF similar to that of a Gaussian random sphere with sigma=0.3, whereas roundish grain shapes are best fitted with that of a fractal aggregate of a fractal dimension 2.4-1.8. In addition we used a fitting algorithm to obtain the best-fit DFFs for the various laboratory samples. In this way we can independently derive information on the shape of the grains from their infrared spectra. For anisotropic materials, different DFFs are needed for the different crystallographic axes. This is due to a theoretical problem, which is inherent to all models that are simply averaging the contributions of the crystallographic directions.