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Thermal imaging of dust hiding the black hole in NGC 1068

This article has been updated

Abstract

In the widely accepted ‘unified model’1 solution of the classification puzzle of active galactic nuclei, the orientation of a dusty accretion torus around the central black hole dominates their appearance. In ‘type-1’ systems, the bright nucleus is visible at the centre of a face-on torus. In ‘type-2’ systems the thick, nearly edge-on torus hides the central engine. Later studies suggested evolutionary effects2 and added dusty clumps and polar winds3 but left the basic picture intact. However, recent high-resolution images4 of the archetypal type-2 galaxy NGC 10685,6, suggested a more radical revision. The images displayed a ring-like emission feature that was proposed to be hot dust surrounding the black hole at the radius where the radiation from the central engine evaporates the dust. That ring is too thin and too far tilted from edge-on to hide the central engine, and ad hoc foreground extinction is needed to explain the type-2 classification. These images quickly generated reinterpretations of the dichotomy between types 1 and 27,8. Here we present new multi-band mid-infrared images of NGC 1068 that detail the dust temperature distribution and reaffirm the original model. Combined with radio data (J.F.G. and C.M.V.I., manuscript in preparation), our maps locate the central engine that is below the previously reported ring and obscured by a thick, nearly edge-on disk, as predicted by the unified model. We also identify emission from polar flows and absorbing dust that is mineralogically distinct from that towards the Milky Way centre.

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Fig. 1: IRBis reconstructed images of NGC 1068.
Fig. 2: Apertures for the extraction of infrared SEDs.
Fig. 3: Blackbody SED fits.
Fig. 4: Comparison of infrared and radio images.

Data availability

The raw MATISSE data used in this article are available to qualified researchers at http://archive.eso.org/eso/eso_archive_main.html. Reduced data are available at https://github.com/VioletaGamez/NGC1068_MATISSE.

Code availability

The Python code for the emcee sampler is available via https://emcee.readthedocs.io. The Python code to fit multi-Gaussian models, and spectral energy distributions, is available at https://doi.org/10.5281/zenodo.5599363. The MiRA image reconstruction code is available at https://github.com/emmt/MiRA. The ESO MATISSE pipeline, including IRBis, is available from https://www.eso.org/sci/software/pipelines/matisse/matisse-pipe-recipes.html.

Change history

  • 22 March 2022

    In the version of this article initially published, the yellow ellipses in Fig. 1e and the white contours in Fig. 4c drifted from their correct placement in the images. The errors affect presentation only and not underlying results, but are corrected here to improve clarity.

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Acknowledgements

We thank ESO and particularly the Cerro Paranal staff for their support in obtaining these observations. The data presented here were taken as part of ESO projects 60.A-9257(commissioning) and 0104.B-0322(A) (MATISSE Guaranteed Time Observations of AGNs). We thank the GRAVITY AGN team for useful scientific discussions and early access to digital versions of their data (programme IDs 0102.B-0667, 0102.C-0205 and 0102.C-0211). MATISSE was defined, funded and built in close collaboration with ESO, by a consortium composed of French (INSU-CNRS in Paris and OCA in Nice), German (MPIA, MPIfR and University of Kiel), Dutch (NOVA and University of Leiden), and Austrian (University of Vienna) institutes. The Conseil Départemental des Alpes-Maritimes in France, the Konkoly Observatory and Cologne University have also provided resources for the manufacture of the instrument. A thought goes to our two deceased OCA colleagues, Olivier Chesneau and Michel Dugué, with us at the origin of the MATISSE project, and with whom we shared many beautiful moments. V.G.R. was partially supported by the Netherlands Organisation for Scientific Research (NWO). J.H.L. acknowledges the support of the French government through the UCA JEDI Investment in the Future project managed by the National Research Agency (ANR) under the reference number ANR-15-IDEX-01.

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Contributions

V.G.R., J.W.I., W.J., R.G.P., K.-H.H., F.M., J.H.L., A. Meilland: observing, data reduction, calibration, modelling, interpretation. B.L., S.L., F.A., S.R.-D., P.C., P. Berio, F.B., T.H., G.W., P.A., U.B., U.G., M. Heininger, M.L., A. Matter, D.S., P.S., J.W., G.Z., P. Bendjoya: MATISSE instrument design, fabrication, and commissioning, calibration. L.B., G.W., R.v.B., P.S., J.-C.A., M. Hogerheijde, J.-U.P.: scientific planning. J.F.G., C.M.V.I., K.T., L.B., C.P.: observing. W.C.D., C.D., J.D., V.H., J.H., L.K., E.K., L.L., E.P., A.S., J.V., S.W., L.B.F.M.W., G.Y.: interpretation.

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Correspondence to Violeta Gámez Rosas.

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Nature thanks Robert Antonucci and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 MATISSE faint calibrator data and uv coverage.

a,Instrumental squared visibility (ISV) and b, non-calibrated closure phases (T3PHI) of calibrators observed during the months of September 2018, May 2019 and June 2019, at 3.4 μm. Data points are colour-coded by their diameters, and the size of the circles correspond to the average coherence times of the observations. The vertical blue strip covers the approximate correlated flux of NGC 1068 at the same wavelength. Error bars show one standard deviation of data. c, uv coverage of MATISSE observations.

Extended Data Fig. 2 Comparison of photometry of the models and image reconstructions.

The photometry between methods generally agrees in spectral shape between different methods. The most notable exceptions are SE and E3 which still produce similar temperatures from SED modelling between methods.

Extended Data Fig. 3 SED black body model fits to MATISSE aperture photometry.

The figures are labelled with the aperture names defined in Fig. 2. The shaded areas show all models falling inside 1 sigma of the photometry, considering both pure amorphous olivine (magenta) and a mix of olivine and 20% amorphous carbon by weight (cyan). The plots for apertures E1 and SE are in the main article.

Extended Data Fig. 4 NGC 1068 N-band data compared to best multi-Gaussian model.

a, Squared visibilities for NGC 1068. The blue lines show observed values, averaged over sub-exposures; the thin grey lines show individual sub-exposures in order to illustrate the measurement uncertainties, but are often hidden behind the blue lines. The green points with error bars show values predicted by the multi-Gaussian models from Methods. The error bars represent the r.m.s. sum of the measurement errors and the uncertainties of the model parameters. The distance between models and observations shows that a limited number of Gaussians cannot exactly represent the true sky or that we do not have a sufficient uv coverage and/or resolution. The grey bands mark the atmospheric non-transmission band. The labels indicate the telescope pairs for each baseline, the baseline length (m) and position angle (degrees), and the specific exposure label from the observation log described in the main paper. b, Closure phases (degrees) using the same colour code as above. The labels indicate the telescope triplets and the specific exposure label from the observing log.

Extended Data Fig. 5 NGC 1068 spectra.

a, Average single telescope spectrum of NGC 1068 in LM-bands (black solid line) and N-band (blue solid line). The error bars represent uncertainties estimated from the differences between different dates and calibrators. The yellow stars refer to VLT/ISAAC L’ -and M-band single-dish flux estimates from ref. 63, while the green triangle corresponds to a VLT/NACO M-band flux from ref. 62. b, The silicate absorption feature observed on two baselines at high spectral resolution (R ~ 300) during a single MATISSE commissioning snapshot. The 85 m baseline shows the broader, double-peaked profile characteristic of crystalline, reprocessed grains61. The difference between the curves shows that the crystallinity varies over the source.

Extended Data Fig. 6 MATISSE N-band squared visibilities and closure phases.

The quantities plotted, and the symbols used are the same as Extended Data Fig. 4 for the N band.

Extended Data Fig. 7 Comparison of reconstructed images at four wavelengths from four algorithms.

From left to right: the MIRA image reconstruction, the IRBis image reconstruction, the overfitted point source model (convolved with the beam), and Gaussian model for four selected wavelengths. The plot uses a 0.6 power colour scaling for visual purposes. Each method reveals similar structures and morphology.

Extended Data Fig. 8 Evaluation of artefacts created by IRBis image reconstruction.

In order to quantify the fidelity of the reconstructions shown in Fig. 1, we performed analogous reconstruction on an artificial model. The model consisted of seven Gaussians, similar to our multi-Gaussian model for the dust emission (Methods). We simulated visibility and closure phase data for this model for our uv coverage; we added noise to the simulated data similar to that in the observations. We then performed image reconstruction using IRBis with identical reconstruction parameters to those used in Fig. 1. a, The input 7-Gaussian model. b, The IRBis reconstructed image. c, The reconstructed image minus the input model. In all cases the colour scale represents the fraction of the peak intensity of the original model. The r.m.s. errors in the residual maps were 2.3% of the peak brightness. This indicated that most of the artefacts present in Fig. 1 result from the uv coverage rather than noise on the observed quantities. In Fig. 1 we have drawn white contours at 3σ = 6% of the peak. Features brighter than this certainly represent true source emission.

Extended Data Fig. 9 NGC 1068 image reconstruction data.

a, Image representation of models obtained from the Gaussian modelling approach described in Methods. We use square root intensity scale. b, Image reconstruction with MIRA using different methods (see Methods). From left to right: using total variation (TV) regularizer, on a large bandwidth ('grey' reconstruction); using the same regularizer, but independently reconstructing images at each wavelength, and computing a median over the wavelength interval; using maximum entropy regularizer (grey reconstruction); using smoothness regularizer (grey reconstruction).

Extended Data Table 1 Parameters for IRBis image reconstruction, Gaussian modelling and SED fitting

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Gámez Rosas, V., Isbell, J.W., Jaffe, W. et al. Thermal imaging of dust hiding the black hole in NGC 1068. Nature 602, 403–407 (2022). https://doi.org/10.1038/s41586-021-04311-7

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