Near-infrared scattering as a dust diagnostic

TL;DR

This study explores how near-infrared scattering measurements can be used to infer dust properties and radiation fields in molecular clouds, highlighting the sensitivity of these inferences to observational uncertainties.

Contribution

It demonstrates the potential of near-infrared scattering data to constrain dust grain size distributions and radiation field strength, emphasizing the importance of accurate surface brightness measurements.

Findings

Surface brightness ratios vary with dust properties.

Small measurement errors significantly impact parameter estimates.

Estimated parameters have ~25% uncertainty on average.

Abstract

We examine the possibility of using near-infrared scattering to constrain the local radiation field and the dust properties, for example, the size distribution of the grains, and maximum grain size. We use radiative transfer modelling to examine the constraints provided by J, H, and K bands in combination with mid-infrared surface brightness at 3.6 $\mu$m. We use a spherical one-dimensional and elliptical three-dimensional cloud models to study the observable effects of different grain size distributions with varying absorption and scattering properties. As an example, we analyse observations of a molecular cloud in Taurus, TMC-1N. The observed surface brightness ratios between the bands change when the dust properties are changed. However, even a small change of 10% in the surface brightness of one channel changes the estimated powerlaw exponent of the size distribution $\gamma$ by up to ~30% and the estimated strength of the radiation field K_ISRF by up to ~60%. The maximum grain size A_max and $\gamma$ are always strongly anti-correlated. For example, overestimating the surface brightness by 10% changes the estimated radiation field strength by ~20% and the exponent of the size distribution by ~15%. The analysis of our synthetic observations indicates that the relative uncertainty of the parameter distributions are on average A_max, $\gamma$ ~25%, and the deviation between the estimated and correct values $\Delta$ Q <15%. We show that scattered infrared light can be used to derive meaningful limits for the dust parameters. However, errors in the surface brightness data can result in considerable uncertainties in the derived parameters.