GoMar23 model: The Role of Grain size in AGN torus dust models
Active galactic nuclei (AGN) are surrounded by dust within the central parsecs. The dusty circumnuclear structures, referred to as the torus, are mainly heated by radiation from the AGN and emitted at infrared wavelengths, producing the emergent dust continuum and silicate features. Fits to the infrared spectra from the nuclear regions of AGN can place constraints on the dust properties, distribution, and geometry by comparison with models. However, none of the currently available models fully describe the observations of AGN currently available.
Among the aspects least explored, here we focus on the role of dust grain size. We offer the community a new spectral energy distribution (SED) library which is based on the two-phase torus model developed before with the inclusion of the grain size as a model parameter, parameterized by the maximum grain size Psize or equivalently the mass-weighted average grain size ⟨P⟩. We created 691 200 SEDs using the SKIRT code, where the maximum grain size can vary within the range Psize = 0.01 − 10.0 μm (⟨P⟩ = 0.007 − 3.41 μm). We fit this new and several existing libraries to a sample of 68 nearby and luminous AGN with Spitzer/IRS spectra dominated by AGN-heated dust.
The GoMar23 model can adequately reproduce up to ∼85-88% of the spectra. The dust grain size parameter significantly improves the final fit in up to 90% of these spectra. In Table 1 you can find a description of the parameters of the model in comparison with the two-phase model (Stalevsky et al. 2016). This web page includes the library in Xspec format for the community.
See “https://ui.adsabs.harvard.edu/abs/2023A%26A…676A..73G/abstract” for more details.
Xspec library:
The library can be downloaded here: https://www.dropbox.com/scl/fi/ul3marpyc1axux3jc3ywk/GoMar23_model.fits?rlkey=pdw9vgd8kk9d27prust1i74sq&dl=0
Note that this library is 32GB in size.
In order to upload the model into Xspec write:
atable{GoMar23_model.fits}
The screenshot below shows the model’s appearance. With ZDUST, the foreground dust model (Pei 1992) can be included. Please review the Xspec manual for more information on loading and fitting the data.
Python library:
(to be included)