Published August 22, 2022 | Version v1
Journal article Open

Data products of "The demographics of obscured AGN from X-ray spectroscopy guided by multiwavelength information"

  • 1. National observatory of Athens
  • 2. National Observatory of Athens
  • 3. Durham University
  • 4. Newcastle University,
  • 5. University of Edinburgh
  • 6. Max Planck Institute
  • 7. Instituto de Física de Cantabria
  • 8. SISSA
  • 9. Instituto de Astrofísica de Canarias
  • 10. University of Southampton

Description

In Laloux et al. (2022, in prep), we developed an approach to guide AGN X-ray spectroscopy with multi-wavelength data. The abstract of the paper is in the  additional notes. We extracted and fitted the X-ray spectra of 2965 AGN within the Chandra COSMOS Legacy survey.
Here, we release the data of our results:
- spectral_extraction_info.fits contains the information relative to the spectral extraction and is described and partially displayed in Table 2 of the original paper. The table contains the information of the 2965 sources and has 11 columns: ['ID', 'RA', 'Dec', 'RA_optical', 'DEC_optical', 'EEF', 'radius_src', 'cts057', 'cts_bkg', 'hard_flag', 'ID_L18']. They correspond to:
    (1) source ID;
    (2-3) X-ray position;
    (4-5) optical counterpart position;
    (6) EEF used for extraction in percent units;
    (7) source radius in arcsecond;
    (8) $0.5-7$keV net photon counts of the source;
    (9) the net photon counts of the background in all energy band;
    (10) flag indicating if the source is detected in the hard band (2-7keV);
    (11) source ID in Lanzuisi et al. (2018) if cross-matched.

- fit_info.fits contains the information relative to the X-ray spectroscopic analysis and is described and partially displayed in Table 6 of the original paper. The tables also contains 2965 sources and has 19 columns: ['ID', 'z', 'ztype', 'logLx', 'logLxlo', 'logLxhi', 'logNH', 'logNHlo', 'logNHhi', 'gamma', 'gammalo', 'gammahi', 'CT_candidate', '2-peaked', 'ID_Jin', 'logL6um', 'logL6umlo', 'logL6umhi', 'upperlimits']. They correspond to:
    (1) source ID;
    (2) redshift;
    (3) redshift type: spectroscopic, photometric or None;
    (4-6) X-ray 2-10keV logarithmic luminosity and its 1-$\sigma$ lower and upper limit;
    (7-9) logarithmic column density $N_{\rm H}$ and its 1-$\sigma$ lower and upper limit;
    (10-12) photon index $\Gamma$ and its 1-$\sigma$ lower and upper limit;
    (13) CTK candidate flag i.e. if the original spectroscopic fit includes more than 5\% of its posterior distribution in the CTK regime;
    (14) double-peaked flag (see definition in section \ref{compaLanzuisi_subsec});
    (15) source ID in the multiwavelength catalog Jin et al. (2018) if available;
    (16-18) logarithmic AGN $L_{6\mu m}$ from SED fitting and its 1-$\sigma$ lower and upper limit;
    (19) logarithmic AGN $L_{6\mu m}$ upper limit at 99 percentile if the SED fit is unconstrained.

- lgphi_chains.fits is the space density calculation product. It contains a (1000, 10, 7, 4)-shaped array. The first axis corresponds to the space density posterior distribution for each parameter grid cell. The second, third and fourth axis are the X-ray luminosity, column density and redshift grid cell numbers, respectively. The edges of the grid pixels in each of the three dimensions are log($L_X$)=(40.0, 41.0, 42.0, 42.5, 43.0, 43.5, 44.0, 44.5, 45.0, 46.0, 47.0) [log(erg s$^{-1}$)], $z$=(0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 6.0) and log($N_{\rm H}$)=(20.0, 22.0, 23.0, 24.0, 26.0) [log(cm$^{-2}$)].

- f_CTK_chains.fits is the product of our Compton-thick fraction estimation as a function of the redshift. It contains a (1000, 7)-shaped array. The first axis is the Compton-thick fraction and the second one is the redshift grid cell presented earlier.

- plot_scripts.py is a python script containing the functions used to open the lgphi_chains.fits and f_CTK_chains.fits files. It plots the space density (see figure 17) and Compton-thick fraction evolution (see figure 18) in the paper.

Notes

Abstract of Laloux et al. (2022) A complete census of Active Galactic Nuclei (AGN) is a prerequisite for understanding the growth of supermassive black holes across cosmic time. A significant challenge toward this goal is the whereabouts of heavily obscured AGN that remain uncertain. This paper sets new constraints on the demographics of this population by developing a methodology that combines X-ray spectral information with priors derived from multiwavelength observations. We select X-ray AGN in the Chandra COSMOS Legacy survey and fit their $2.2-500\mu m$ spectral energy distributions with galaxy and AGN templates to determine the mid-infrared ($6\mu m$) luminosity of the AGN component. Empirical correlations between X-ray and $6\mu m$ luminosities are then adopted to infer the intrinsic accretion luminosity at X-rays for individual AGN. This is used as prior information in our Bayesian X-ray spectral analysis to estimate physical properties, such as line-of-sight obscuration. Our approach breaks the degeneracies between accretion luminosity and obscuration that affect X-ray spectral analysis, particularly for the most heavily obscured (Compton-Thick) AGN with low photon counts X-ray spectra. The X-ray spectral results are then combined with the selection function of the Chandra COSMOS Legacy survey to derive the AGN space density and a Compton-Thick fraction of $21.0^{+16.1}_{-9.9}\%$ at redshifts $z<0.5$. At higher redshift, our analysis suggests upper limits to the Compton-Thick AGN fraction of $\la 40\%$. These estimates are at the low end of the range of values determined in the literature and underline the importance of multiwavelength approaches for tackling the challenge of heavily obscured AGN demographics.

Files

READ_ME.txt

Files (23.7 MB)

Name Size Download all
md5:bdca313416b2ed08268cfe45f37c2930
567.4 kB Download
md5:74060d782ab91fa4fd930f1f0a28a6e8
423.4 kB Download
md5:732577eaa565d59d681fd1e3f2ac11e8
22.4 MB Download
md5:7577a4c1642bf047c2de6d99133c86d9
8.2 kB Download
md5:776ef4618fe9035b1ba7c937f6d0e493
3.5 kB Preview Download
md5:7ebc756c77d50321d782f550848e52a8
250.6 kB Download

Additional details

Funding

BiD4BEST – Big Data applications for Black hole Evolution STudies 860744
European Commission