The origin of the unification model for active galactic nuclei (AGN) was the detection of broad hydrogen recombination lines in the optical polarized spectrum of the Seyfert 2 galaxy (Sy2) NGC 1068. ...Since then, a search for the hidden broad-line region (HBLR) of nearby Sy2s started, but polarized broad lines have only been detected in ∼30–40 per cent of the nearby Sy2s observed to date. Here we present new VLT/FORS2 optical spectropolarimetry of a sample of 15 Sy2s, including Compton-thin and Compton-thick sources. The sample includes six galaxies without previously published spectropolarimetry, some of them normally treated as non-hidden BLR (NHBLR) objects in the literature, four classified as NHBLR, and five as HBLR based on previous data. We report ≥4σ detections of a HBLR in 11 of these galaxies (73 per cent of the sample) and a tentative detection in NGC 5793, which is Compton-thick according to the analysis of X-ray data performed here. Our results confirm that at least some NHBLRs are misclassified, bringing previous publications reporting differences between HBLR and NHBLR objects into question. We detect broad Hα and Hβ components in polarized light for 10 targets, and just broad Hα for NGC 5793 and NGC 6300, with line widths ranging between 2100 and 9600 km s−1. High bolometric luminosities and low column densities are associated with higher polarization degrees, but not necessarily with the detection of the scattered broad components.
Aims.
Finding potential life harboring exo-Earths with future telescopes is one of the aims of exoplanetary science. Detecting signatures of life in exoplanets will likely first be accomplished by ...determining the bulk composition of the planetary atmosphere via reflected or transmitted spectroscopy. However, a complete understanding of the habitability conditions will surely require mapping the presence of liquid water, continents, and/or clouds. Spin-orbit tomography is a technique that allows us to obtain maps of the surface of exoplanets around other stars using the light scattered by the planetary surface.
Methods.
We leverage the enormous potential of deep learning, and propose a mapping technique for exo-Earths in which the regularization is learned from mock surfaces. The solution of the inverse mapping problem is posed as a deep neural network that can be trained end-to-end with suitable training data. Since we still lack observational data of the surface albedo of exoplanets, in this work we propose methods based on the procedural generation of planets, inspired by what we have found on Earth. We also consider mapping the recovery of surfaces and the presence of persistent clouds in cloudy planets, a much more challenging problem.
Results.
We show that reliable mapping can be carried out with our approach, producing very compact continents, even when using single-passband observations. More importantly, if exoplanets are partially cloudy like the Earth is, we show that it is possible to map the distribution of persistent clouds that always occur in the same position on the surface (associated with orography and sea surface temperatures) together with nonpersistent clouds that move across the surface. This will become the first test to perform on an exoplanet for the detection of an active climate system. For small rocky planets in the habitable zone of their stars, this weather system will be driven by water, and the detection can be considered a strong proxy for truly habitable conditions.
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We have developed an inversion procedure designed for high-resolution solar spectro-polarimeters, such as those of Hinode and the DKIST. The procedure is based on artificial neural networks trained ...with profiles generated from random atmospheric stratifications for a high generalization capability. When applied to Hinode data, we find a hot fine-scale network structure whose morphology changes with height. In the middle layers, this network resembles what is observed in
G
-band filtergrams, but it is not identical. Surprisingly, the temperature enhancements in the middle and upper photosphere have a reversed pattern. Hot pixels in the middle photosphere, possibly associated with small-scale magnetic elements, appear cool at the log
τ
500
= −3 and −4 level, and vice versa. Finally, we find hot arcs on the limb side of magnetic pores. We interpret them as the first piece of direct observational evidence of the “hot wall” effect, which is a prediction of theoretical models from the 1970’s.
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We present nuclear spectral energy distributions (SEDs) from 1 to 18 μm of a small sample of nearby, nearly face-on and undisturbed Seyfert galaxies without prominent nuclear dust lanes. These ...nuclear SEDs probe the central ∼35 pc of the galaxies, on average, and include photometric and spectroscopic infrared (IR) data. We use these SEDs, the clumpy torus models of Nenkova et al. and a Bayesian approach to study the sensitivity of different IR wavelengths to the torus parameters. We find that high angular resolution 8-13 μm spectroscopy alone reliably constrains the number of clumps and their optical depth (N
0 and τ
V
). On the other hand, we need a combination of mid- and near-IR subarcsecond resolution photometry to constrain torus width and inclination, as well as the radial distribution of the clouds (σ, i and q). For flat radial profiles (q = 0, 1), it is possible to constrain the extent of the mid-IR-emitting dust within the torus (Y) when N-band spectroscopy is available, in addition to near-IR photometry. Finally, by fitting different combinations of average and individual Seyfert 1 and Seyfert 2 data, we find that, in general, for undisturbed, nearly face-on Seyferts without prominent nuclear dust lanes, the minimum combination of data necessary to reliably constrain all the torus parameters is J+K+M-band photometry + N-band spectroscopy.
ABSTRACT We present the distributions of the geometrical covering factors of the dusty tori (f2) of active galactic nuclei (AGNs) using an X-ray selected complete sample of 227 AGNs drawn from the ...Bright Ultra-hard XMM-Newton Survey. The AGNs have z from 0.05 to 1.7, 2-10 keV luminosities between 1042 and 1046 erg s−1, and Compton-thin X-ray absorption. Employing data from UKIDSS, 2MASS, and the Wide-field Infrared Survey Explorer in a previous work, we determined the rest-frame 1-20 m continuum emission from the torus, which we model here with the clumpy torus models of Nenkova et al. Optically classified type 1 and type 2 AGNs are intrinsically different, with type 2 AGNs having, on average, tori with higher f2 than type 1 AGNs. Nevertheless, ∼20% of type 1 AGNs have tori with large covering factors, while ∼23%-28% of type 2 AGNs have tori with small covering factors. Low f2 are preferred at high AGN luminosities, as postulated by simple receding torus models, although for type 2 AGNs the effect is certainly small. f2 increases with the X-ray column density, which implies that dust extinction and X-ray absorption take place in material that share an overall geometry and most likely belong to the same structure, the putative torus. Based on our results, the viewing angle, AGN luminosity, and also f2 determine the optical appearance of an AGN and control the shape of the rest-frame ∼1-20 m nuclear continuum emission. Thus, the torus geometrical covering factor is a key ingredient of unification schemes.
Context.
Observations from ground-based telescopes are severely perturbed by the presence of the Earth’s atmosphere. The use of adaptive optics techniques has allowed us to partly overcome this ...limitation. However, image-selection or post-facto image-reconstruction methods applied to bursts of short-exposure images are routinely needed to reach the diffraction limit. Deep learning has recently been proposed as an efficient way to accelerate these image reconstructions. Currently, these deep neural networks are trained with supervision, meaning that either standard deconvolution algorithms need to be applied a priori or complex simulations of the solar magneto-convection need to be carried out to generate the training sets.
Aims.
Our aim here is to propose a general unsupervised training scheme that allows multiframe blind deconvolution deep learning systems to be trained with observations only. The approach can be applied for the correction of point-like as well as extended objects.
Methods.
Leveraging the linear image formation theory and a probabilistic approach to the blind deconvolution problem produces a physically motivated loss function. Optimization of this loss function allows end-to-end training of a machine learning model composed of three neural networks.
Results.
As examples, we apply this procedure to the deconvolution of stellar data from the FastCam instrument and to solar extended data from the Swedish Solar Telescope. The analysis demonstrates that the proposed neural model can be successfully trained without supervision using observations only. It provides estimations of the instantaneous wavefronts, from which a corrected image can be found using standard deconvolution techniques. The network model is roughly three orders of magnitude faster than applying standard deconvolution based on optimization and shows potential to be used on real-time at the telescope.
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A big challenge in solar and stellar physics in the coming years will be to decipher the magnetism of the solar outer atmosphere (chromosphere and corona) along with its dynamic coupling with the ...magnetic fields of the underlying photosphere. To this end, it is important to develop rigorous diagnostic tools for the physical interpretation of spectropolarimetric observations in suitably chosen spectral lines. Here we present a computer program for the synthesis and inversion of Stokes profiles caused by the joint action of atomic level polarization and the Hanle and Zeeman effects in some spectral lines of diagnostic interest, such as those of the He i 10830 Aa and 5876 Aa (or D sub(3)) multiplets. It is based on the quantum theory of spectral line polarization, which takes into account in a rigorous way all the relevant physical mechanisms and ingredients (optical pumping, atomic level polarization, level crossings and repulsions, Zeeman, Paschen-Back, and Hanle effects). The influence of radiative transfer on the emergent spectral line radiation is taken into account through a suitable slab model. The user can either calculate the emergent intensity and polarization for any given magnetic field vector or infer the dynamical and magnetic properties from the observed Stokes profiles via an efficient inversion algorithm based on global optimization methods. The reliability of the forward modeling and inversion code presented here is demonstrated through several applications, which range from the inference of the magnetic field vector in solar active regions to determining whether or not it is canopy-like in quiet chromospheric regions. This user-friendly diagnostic tool called 'HAZEL' (from HAnle and ZEeman Light) is offered to the astrophysical community, with the hope that it will facilitate new advances in solar and stellar physics.
Many phenomena taking place in the solar photosphere are controlled by plasma motions. Although the line-of-sight component of the velocity can be estimated using the Doppler effect, we do not have ...direct spectroscopic access to the components that are perpendicular to the line of sight. These components are typically estimated using methods based on local correlation tracking. We have designed DeepVel, an end-to-end deep neural network that produces an estimation of the velocity at every single pixel, every time step, and at three different heights in the atmosphere from just two consecutive continuum images. We confront DeepVel with local correlation tracking, pointing out that they give very similar results in the time and spatially averaged cases. We use the network to study the evolution in height of the horizontal velocity field in fragmenting granules, supporting the buoyancy-braking mechanism for the formation of integranular lanes in these granules. We also show that DeepVel can capture very small vortices, so that we can potentially expand the scaling cascade of vortices to very small sizes and durations.
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Context. The analysis of waves on the visible side of the Sun allows the detection of active regions on the far side through local helioseismology techniques. Knowing the magnetism in the whole Sun, ...including the non-visible hemisphere, is fundamental for several space weather forecasting applications. Aims. Seismic identification of far-side active regions is challenged by the reduced signal-to-noise ratio, and only large and strong active regions can be reliable detected. Here we develop a new method to improve the identification of active region signatures in far-side seismic maps. Methods. We constructed a deep neural network that associates the far-side seismic maps obtained from helioseismic holography with the probability that active regions lie on the far side. The network was trained with pairs of helioseismic phase-shift maps and Helioseismic and Magnetic Imager (HMI) magnetograms acquired half a solar rotation later, which were used as a proxy for the presence of active regions on the far side. The method was validated using a set of artificial data, and it was also applied to actual solar observations during the period of minimum activity of solar cycle 24. Results. Our approach shows a higher sensitivity to the presence of far-side active regions than standard methods that have been applied up to date. The neural network can significantly increase the number of detected far-side active regions, and will potentially improve the application of far-side seismology to space weather forecasting.
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We present subarcsecond resolution mid-infrared (mid-IR) photometry in the wavelength range from 8 to 20 Delta *mm of 18 Seyfert galaxies, reporting high spatial resolution nuclear fluxes for the ...entire sample. We construct spectral energy distributions (SEDs) that the active galactic nucleus (AGN) dominates, relatively uncontaminated by starlight, adding near-IR measurements from the literature at similar angular resolution. We find that the IR SEDs of intermediate-type Seyferts are flatter and present higher 10 to 18 Delta *mm ratios than those of Seyfert 2 galaxies. We fit the individual SEDs with clumpy dusty torus models using the in-house-developed BayesClumpy tool. We find that the clumpy models reproduce the high spatial resolution measurements. Regardless of the Seyfert type, even with high spatial resolution data, near- to mid-IR SED fitting poorly constrains the radial extent of the torus. For the Seyfert 2 galaxies, we find that edge-on geometries are more probable than face-on views, with a number of clouds along equatorial rays of N 0 = 5-15. The 10 Delta *mm silicate feature is generally modeled in shallow absorption. For the intermediate-type Seyferts, N 0 and the inclination angle of the torus are lower than those of the Seyfert 2 nuclei, with the silicate feature appearing in weak emission or absent. The columns of material responsible for the X-ray absorption are larger than those inferred from the model fits for most of the galaxies, which is consistent with X-ray absorbing gas being located within the dust sublimation radius, whereas the mid-IR flux arises from an area farther from the accretion disk. The fits yield both the bolometric luminosity of the intrinsic AGN and the torus-integrated luminosity, from which we derive the reprocessing efficiency of the torus. In the models, the outer radial extent of the torus scales with the AGN luminosity, and we find the tori to be confined to scales less than 5 pc.
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