Inferring the morphology of AGN torus using X-ray spectra: A reliability study

TL;DR

This study evaluates the reliability of various X-ray spectral models in accurately inferring the morphology of AGN tori, highlighting limitations due to data quality and model degeneracies.

Contribution

It systematically tests the ability of different spectral models to recover AGN torus parameters and distinguish models using Bayesian analysis on simulated X-ray spectra.

Findings

Many parameters are poorly constrained with current data.

Bayesian evidence can distinguish correct from incorrect models with sufficient energy coverage.

Parameter constraints depend on instrument combination and source flux.

Abstract

Numerous X-ray spectral models have been developed to model emission reprocessed by the torus of an active galactic nucleus (AGN), e.g., UXCLUMPY, CTORUS, and MYTORUS. They span a range of assumed torus geometries and morphologies-some posit smooth gas distributions, and others posit distributions of clouds. It is suspected that given the quality of currently available data, certain model parameters, such as coronal power law photon index and parameters determining the morphology of the AGN torus, may be poorly constrained due to model degeneracies. In this work, we test the reliability of these models in terms of recovery of parameters and the ability to discern the morphology of the torus using XMM-Newton and NuSTAR spectral data. We perform extensive simulations of X-ray spectra of Compton-thick AGNs under six X-ray spectral models of the torus. We use Bayesian methods to investigate degeneracy between model parameters, distinguish models and determine the dependence of the parameter constraints on the instruments used. For typical exposure times and fluxes for nearby Compton-thick AGN, we find that several parameters across the models used here cannot be well constrained, e.g., the distribution of clouds, the number of clouds in the radial direction, even when the applied model is correct. We also find that Bayesian evidence values can robustly distinguish between a correct and a wrong model only if there is sufficient energy coverage and only if the intrinsic flux of the object is above a particular value determined by the instrument combination and the model considered.