X-ray spectral modelling of the AGN obscuring region in the CDFS: Bayesian model selection and catalogue

By J. Buchner, A. Georgakakis, K. Nandra, L. Hsu, C. Rangel, M. Brightman, A. Merloni, Mara Salvato, J. Donley, D. Kocevski. 2014

1,105 citations recorded by the source corpus.

Aims. Active galactic nuclei are known to have complex X-ray spectra that depend on both the properties of the accreting super-massive black hole (e.g. mass, accretion rate) and the distribution of obscuring material in its vicinity (i.e. the “torus”). Often however, simple and even unphysical models are adopted to represent the X-ray spectra of AGN, which do not capture the complexity and diversity of the observations. In the case of blank field surveys in particular, this should have an impact on e.g. the determination of the AGN luminosity function, the inferred accretion history of the Universe and also on our understanding of the relation between AGN and their host galaxies. Methods. We develop a Bayesian framework for model comparison and parameter estimation of X-ray spectra. We take into account uncertainties associated with both the Poisson nature of X-ray data and the determination of source redshift using photometric methods. We also demonstrate how Bayesian model comparison can be used to select among ten di erent physically motivated X-ray spectral models the one that provides a better representation of the observations. This methodology is applied to X-ray AGN in the 4 Ms Chandra Deep Field South. Results. For the 350 AGN in that field, our analysis identifies four components needed to represent the diversity of the observed X-ray spectra: (1) an intrinsic power law; (2) a cold obscurer which reprocesses the radiation due to photo-electric absorption, Compton scattering and Fe-K fluorescence; (3) an unabsorbed power law associated with Thomson scattering o ionised clouds; and (4) Compton reflection, most noticeable from a stronger-than-expected Fe-K line. Simpler models, such as a photo-electrically absorbed power law with a Thomson scattering component, are ruled out with decisive evidence (B > 100). We also find that ignoring the Thomson scattering component results in underestimation of the inferred column density, NH, of the obscurer. Regarding the geometry of the obscurer, there is strong evidence against both a completely closed (e.g. sphere), or entirely open (e.g. blob of material along the line of sight), toroidal geometry in favour of an intermediate case. Conclusions. Despite the use of low-count spectra, our methodology is able to draw strong inferences on the geometry of the torus. Simpler models are ruled out in favour of a geometrically extended structure with significant Compton scattering. We confirm the presence of a soft component, possibly associated with Thomson scattering o ionised clouds in the opening angle of the torus. The additional Compton reflection required by data over that predicted by toroidal geometry models, may be a sign of a density gradient in the torus or reflection o the accretion disk. Finally, we release a catalogue of AGN in the CDFS with estimated parameters such as the accretion luminosity in the 2 10 keV band and the column density, NH, of the obscurer.

Subfields: Astronomy and Astrophysics

View Paper: https://www.semanticscholar.org/paper/847aa208f4eb113207386c787018344430985388

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Astronomy and Astrophysics

Citations in the corpus

MontePython 3: Boosted MCMC sampler and other features (2018, methodology): “MontePython also supports sampling with MultiNest (--method NS) [35–37] via a python wrapper [38] and emcee [39–41] via CosmoHammer (--method CH) [41].”
Cosmology from cosmic shear power spectra with Subaru Hyper Suprime-Cam first-year data (2018, methodology): “…of parameters given the cosmic shear signal with the help of nested sampling as implemented in the publicly available Multinest (MultiModal Nested Sampler) code (Feroz & Hobson 2008; Feroz et al. 2009, 2013; Buchner et al. 2014) together with the package Monte Python (Audren et al. 2013).”
Cosmic X-ray surveys of distant active galaxies (2015, background): “…AGNs and limited photon statistics in absorption spectral modeling; at faint X-ray fluxes currently only crude hardness-ratio based absorption estimates can be effectively used, and these cannot appropriately characterize complex X-ray absorption (e.g., Mayo and Lawrence 2013; Buchner et al 2014).”
KiDS-450: The tomographic weak lensing power spectrum and constraints on cosmological parameters (2017, methodology): “Conveniently, its PYTHON-wrapper PYMULTINEST (Buchner et al. 2014) is included in the framework of the cosmological likelihood sampling package MONTE PYTHON10 (Audren et al. 2013) with which we derive all cosmology-related results in this analysis.”
Revealing the dark matter halo with axion direct detection (2017, methodology): “This analysis was performed using MULTINEST [95,96] with 500 live points.”
THE INFLUENCE OF NONUNIFORM CLOUD COVER ON TRANSIT TRANSMISSION SPECTRA (2015, methodology): “We employ PyMultiNest4 (Buchner et al. 2014), a python wrapper to the commonly used MultiNest (Feroz et al. 2009) nested sampling algorithm, to explore the parameter degeneracies.”
FALSE POSITIVE PROBABILITIES FOR ALL KEPLER OBJECTS OF INTEREST: 1284 NEWLY VALIDATED PLANETS AND 428 LIKELY FALSE POSITIVES (2016, methodology): “This fit is done using multi-modal nested sampling, implemented with MultiNest (Feroz et al. 2009, 2011, 2013), via the PyMultiNest wrapper (Buchner et al. 2014).”
Retrieving scattering clouds and disequilibrium chemistry in the atmosphere of HR 8799e (2020, methodology): “…clouds and disequilibrium chemistry in the atmosphere of HR 8799e To derive the posterior abundances of our fit we used the PyMultiNest5 package (Buchner et al. 2014), which is a Python wrapper of the MultiNest method (Feroz & Hobson 2008; Feroz et al. 2009, 2019) for nested sampling (Skilling…”
Bayesian inference for compact binary coalescences with bilby: validation and application to the first LIGO–Virgo gravitational-wave transient catalogue (2020, background): “The pymultinest sampler (Feroz & Hobson 2008; Feroz et al. 2009, 2013; Buchner et al. 2014) can implement periodic boundary conditions, but not reflective, which are treated as None.”
H2O abundances and cloud properties in ten hot giant exoplanets (2018, methodology): “MultiNest is implemented through a python wrapper, PyMultiNest (Buchner et al. 2014).”
A NICER View of PSR J0030+0451: Millisecond Pulsar Parameter Estimation (2019, methodology): “6 can be located at https://github.com/JohannesBuchner/PyMultiNest SHA1-hash 5d8c103.”
Measuring the Binary Black Hole Mass Spectrum with an Astrophysically Motivated Parameterization (2018, methodology): “} ∣ ) ({ } ∣ ) ( ∣ ) ( ) To perform the (hyper)parameter estimation we use the Python implementation of MultiNest (Feroz et al. 2009; Buchner et al. 2014).”
OBSCURATION-DEPENDENT EVOLUTION OF ACTIVE GALACTIC NUCLEI (2015, methodology): “Each X-ray spectrum was analysed with spectral models following the Bayesian methodology of Buchner et al. (2014).”
KiDS+VIKING-450: Cosmic shear tomography with optical and infrared data (2018, methodology): “This setup is based on the nested sampling algorithm MultiNest (Feroz et al. 2009) as implemented in the python wrapper PyMultiNest (Buchner et al. 2014) that is included in the MontePython package (Audren et al. 2013; Brinckmann & Lesgourgues 2018).”
MadDM v.3.0: A comprehensive tool for dark matter studies (2018, methodology): “b_ddx # [21] : xxdxxdb_uux # [22] : lim_xxdxxdb_uux # [23] : xxdxxdb_bbx # [24] : lim_xxdxxdb_bbx # [25] : xxdxxdb_xxcxxcb # [26] : lim_xxdxxdb_xxcxxcb # [27] : xxdxxdb_ssx # [28] : lim_xxdxxdb_ssx # [29] : xxdxxdb_ttx # [30] : lim_xxdxxdb_ttx # [31] : xxdxxdb_xxrxxr # [32] : lim_xxdxxdb_xxrxxr # [3”