Towards an observationally motivated AGN dusty torus model. I. Dust chemical composition from the modeling of Spitzer spectra

TL;DR

This study uses spectral energy distribution fitting of Spitzer spectra to investigate the dust chemical composition in AGN dusty tori, revealing that porous alumina, periclase, and olivine dominate, with minimal astronomical silicates.

Contribution

It introduces a mineralogy model considering various dust species and grain sizes, highlighting the importance of dust composition in radiative transfer models for AGN.

Findings

Porous alumina, periclase, and olivine best fit the spectra.

Dust is predominantly composed of 0.1 μm grains.

Astronomical silicates are rarely present in the best fits.

Abstract

Spectral energy distribution (SED) fitting is one of most commonly used techniques to study the dust properties in Active Galactic Nuclei (AGN). Works implementing this technique commonly use radiative transfer models that assume a variety of dust properties. Despite the key role of this aspect, limited effort has been put forward to explore the chemical composition, the role of different optical properties, and the grain size distribution of dust, all of which can have a substantial impact on the theoretical radiative transfer calculations. In this work, we explore the role of the dust chemical composition in the AGN dusty torus through SED fitting to \emph{Spitzer}/IRS spectra of a sample of 49 nearby AGN with silicate features in emission. We implement a mineralogy model including the popular astronomical silicates and a set of oxides and amorphous silicates with different grain sizes. We find that best fits use principally porous alumina, periclase, and olivine. In terms of mass fractions, $\sim99\%$ of the dust is composed of dust grains of size $\rm{0.1 \mu m}$, with a $<1\%$ contribution from $\rm{3 \mu m}$ grains. Moreover, the astronomical silicates have a very low occurrence in the best fits, suggesting that they are not the most suited dust species to reproduce the silicate features in our sample.