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Full-Stokes polarimetric datasets, originating from slit-spectrograph or narrow-band filtergrams, are routinely acquired nowadays. The data rate is increasing with the advent of bi-dimensional spectropolarimeters and observing techniques that allow long-time sequences of high-quality observations. There is a clear need to go beyond the traditional pixel-by-pixel strategy in spectropolarimetric inversions by exploiting the spatiotemporal coherence of the inferred physical quantities. We explore the potential of neural networks as a continuous representation of the physical quantities over time and space (also known as neural fields), for spectropolarimetric inversions. We have implemented and tested a neural field to perform the inference of the magnetic field vector (approach also known as physics-informed neural networks) under the weak-field approximation (WFA). By using a neural field to describe the magnetic field vector, we can regularize the solution in the spatial and temporal domain by assuming that the physical quantities are continuous functions of the coordinates. We investigated the results in synthetic and real observations of the Ca II 8542 A line. We also explored the impact of other explicit regularizations, such as using the information of an extrapolated magnetic field, or the orientation of the chromospheric fibrils. Compared to the traditional pixel-by-pixel inversion, the neural field approach improves the fidelity of the reconstruction of the magnetic field vector, especially the transverse component. This implicit regularization is a way of increasing the effective signal-to-noise of the observations. Although it is slower than the pixel-wise WFA estimation, this approach shows a promising potential for depth-stratified inversions, by reducing the number of free parameters and inducing spatio-temporal constraints in the solution., Comment: 12 pages, 7 figures, Appendix, Submitted to A&A

Investigating light bridges (LBs) helps us comprehend key aspects of sunspots. However, few studies have analyzed the properties of LBs in terms of the geometric height, which is a more realistic perspective given the corrugation of the solar atmosphere. We aim to shed light on LBs by studying the variation in their physical properties with geometric height. We used the SICON code to infer the physical quantities in terms of the optical depth and the Wilson depression values of three LBs hosted by a sunspot observed with Hinode/SP in the Fe I 630 nm pair lines. We also used SIR inversions to cross-check the height variation of the field inclination in the LBs. In both output sets, we performed linear interpolation to convert the physical parameters from optical depth into a geometric height scale in each pixel. We classified each LB as filamentary, grainy, or umbral. They appear as ridges that reach different maximum heights, with the umbral LB being the deepest. While the filamentary LB hosts a plasma inflow from the penumbra, the results for the grainy LB are compatible with an injection of hot plasma through convective cells of reduced field strength. Only a few positions reveal hints suggesting a cusp-like magnetic canopy. Moreover, strong gradients in the magnetic field strength and inclination usually exhibit enhanced electric currents, with the filamentary LB having remarkably strong currents that appear to be related to chromospheric events. The height stratification in filamentary and grainy LBs differ, indicating diverse mechanisms at work. Our results are in general incompatible with a magnetic canopy scenario, and further analysis is needed to confirm whether it exists along the entire LB or only at specific locations. Furthermore, this work assesses the usefulness of SICON when determining the height stratification of solar structures., Comment: 13 pages, 12 figures. Accepted for publication in A&A. Abstract has been abridged

Astrochemical models are important tools to interpret observations of molecular and atomic species in different environments. However, these models are time-consuming, precluding a thorough exploration of the parameter space, leading to uncertainties and biased results. Using neural networks to simulate the behavior of astrochemical models is a way to circumvent this problem, providing fast calculations that are based on real astrochemical models. In this paper, we present a fast neural emulator of the astrochemical code Nautilus based on conditional neural fields. The resulting model produces the abundance of 192 species for arbitrary times between 1 and 10$^7$ years. Uncertainties well below 0.2 dex are found for all species, while the computing time is of the order of 10$^4$ smaller than Nautilus. This will open up the possibility of performing much more complex forward models to better understand the physical properties of the interstellar medium. As an example of the power of these models, we ran a feature importance analysis on the electron abundance predicted by Nautilus. We found that the electron density is coupled to the initial sulphur abundance in a low density gas. Increasing the initial sulphur abundance from a depleted scenario to the cosmic abundance leads to an enhancement of an order of magnitude of the electron density. This enhancement can potentially influence the dynamics of the gas in star formation sites., Comment: 12 pages, 8 figures, accepted for publication in MNRAS

Both magnetic fields and photospheric/atmospheric dynamics can be involved in triggering the important mass loss observed in evolved cool stars. Previous works have revealed that these objects exhibit a magnetic field extending beyond their surface. The origin of this magnetic field is still under debate with mechanisms involving a turbulent dynamo, convection, stellar pulsation, and cool spots. Our goal is to estimate the magnetic field strength in the inner circumstellar envelope of six evolved cool stars (five Miras and one Red Supergiant). Combining this work with previous studies, we tentatively constrain the global magnetic field type observed and shed light on the mechanisms at its origin. Using the XPOL polarimeter installed at the IRAM-30 m telescope, we observed the 28 SiO v = 1, J = 2-1 maser line emission and obtained simultaneous spectroscopic measurements of the four Stokes parameters. Applying a careful calibration method for Stokes Q, U, and V, we derive estimates of the magnetic field strength from the circular and linear polarization fractions considering the saturated and unsaturated maser cases under the Zeeman hypothesis. Magnetic field strengths from several Gauss up to several tens of Gauss are derived. These new and more accurate measurements constraining the field strength in the 2-5 stellar radii region better than previous studies and seem to exclude a global poloidal magnetic field type. A combination of a toroidal and a poloidal field is nevertheless not excluded. A variation of the magnetic field strength over a two-months timescale is observed in one Mira star which suggests a possible link to the stellar phase, i.e. with pulsation/photospheric activity., Comment: 18 pages, 12 figures, Accepted in A&A

The study of astronomical phenomena through ground-based observations is always challenged by the distorting effects of Earth's atmosphere. Traditional methods of post-facto image correction, essential for correcting these distortions, often rely on simplifying assumptions that limit their effectiveness, particularly in the presence of spatially variant atmospheric turbulence. Such cases are often solved by partitioning the field-of-view into small patches, deconvolving each patch independently, and merging all patches together. This approach is often inefficient and can produce artifacts. Recent advancements in computational techniques and the advent of deep learning offer new pathways to address these limitations. This paper introduces a novel framework leveraging a deep neural network to emulate spatially variant convolutions, offering a breakthrough in the efficiency and accuracy of astronomical image deconvolution. By training on a dataset of images convolved with spatially invariant point spread functions and validating its generalizability to spatially variant conditions, this approach presents a significant advancement over traditional methods. The convolution emulator is used as a forward model in a multi-object multi-frame blind deconvolution algorithm for solar images. The emulator enables the deconvolution of solar observations across large fields of view without resorting to patch-wise mosaicking, thus avoiding artifacts associated with such techniques. This method represents a significant computational advantage, reducing processing times by orders of magnitude., Comment: 15 pages, 14 figures, accepted for publication in A&A

The hydrogen and water molecules respond very differently to the collisional-radiative processes taking place in planetary atmospheres. Naturally, the question arises whether H2O-rich atmospheres are more (or less) resilient to long-term mass loss than H2-dominated ones if they radiate away the incident stellar energy more (or less) efficiently. If confirmed, the finding would have implications on our understanding of the evolution of sub-Neptune exoplanets. As a key step towards answering this question, we present a non-local thermodynamic equilibrium (NLTE) model of H2O for the atmospheric region where the gas accelerates to escape the planet and conditions relevant to close-in sub-Neptunes. Our exploratory calculations for isothermal gas composed of H2, H2O and e- reveal that: 1) In the pressure region ~1e-2 - 1e-4 dyn cm-2 where the stellar extreme-ultraviolet (XUV) photons are typically deposited in the atmosphere, H2O is in rotational LTE but vibrational NLTE. Vibrational LTE is facilitated by high H2O abundances and fractional ionizations, and we report critical densities for the LTE-NLTE transition; 2) Vibrational cooling may locally dominate over rotational cooling, partly because of the comparatively small opacities of ro-vibrational lines; 3) Even low H2O abundances notably enhance the cooling, foreseeably offsetting some of the stellar heating; 4) Heating due to the deposition of stellar infrared (IR) photons is significant at pressures >=0.1 dyn cm-2. We estimate the contribution of H2O excitation to the internal energy of the gas and speculate on the photodissociation from the excited vibrational states. Ultimately, our findings motivate the consideration of NLTE in the mass loss rate calculations of H2O-rich atmospheres., Comment: Accepted for publication in Icarus

Several studies have reported magnetic field fluctuations associated with umbral shock waves. We aim to study the properties and origin of magnetic field fluctuations in the umbral chromosphere. Temporal series of spectropolarimetric observations were acquired with the GREGOR telescope. The chromospheric and photospheric conditions were derived from simultaneous inversions of the He I 10830 \AA\ triplet and the Si I 10827 \AA\ line using HAZEL2. The oscillations are interpreted using wavelet analysis and context information from UV observations acquired with SDO/AIA and IRIS. The chromospheric magnetic field shows strong fluctuations in the sunspot umbra, with peak field strengths up to 2900 G. Magnetic field and velocity umbral oscillations exhibit a strong coherence, with the magnetic field lagging the shock fronts detected in the velocity fluctuations. This points to a common origin of the fluctuations in both parameters, whereas the analysis of the phase shift between photospheric and chromospheric velocity is consistent with upwards wave propagation. These results suggest that the strong inferred magnetic field fluctuations are caused by changes in the response height of the He I 10830 \AA\ line to the magnetic field, which is sensitive to high photospheric layers after the shock fronts. The coronal activity seen in EUV data could possibly have some impact on the inferred fluctuations, but it is not the main driver of the magnetic field oscillations since they are found before EUV events take place. Chromospheric magnetic field fluctuations measured with the He I 10830 \AA\ triplet arise due to variations in the opacity of the line. After shocks produced by slow magnetoacoustic waves, the response of the line to the magnetic field can be shifted down to the upper photosphere. This is seen as remarkably large fluctuations in the line of sight magnetic field strength., Comment: Accepted for publication in A&A. Abstract abridged due to arXiv's 1920 character limit

The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field., Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP)

Ground-based solar image restoration is a computationally expensive procedure that involves nonlinear optimization techniques. The presence of atmospheric turbulence produces perturbations in individual images that make it necessary to apply blind deconvolution techniques. These techniques rely on the observation of many short exposure frames that are used to simultaneously infer the instantaneous state of the atmosphere and the unperturbed object. We have recently explored the use of machine learning to accelerate this process, with promising results. We build upon this previous work to propose several interesting improvements that lead to better models. As well, we propose a new method to accelerate the restoration based on algorithm unrolling. In this method, the image restoration problem is solved with a gradient descent method that is unrolled and accelerated aided by a few small neural networks. The role of the neural networks is to correct the estimation of the solution at each iterative step. The model is trained to perform the optimization in a small fixed number of steps with a curated dataset. Our findings demonstrate that both methods significantly reduce the restoration time compared to the standard optimization procedure. Furthermore, we showcase that these models can be trained in an unsupervised manner using observed images from three different instruments. Remarkably, they also exhibit robust generalization capabilities when applied to new datasets. To foster further research and collaboration, we openly provide the trained models, along with the corresponding training and evaluation code, as well as the training dataset, to the scientific community., Comment: 26 pages, 9 figures, accepted for publication in Solar Physics

Given the widespread availability of grids of models for stellar atmospheres, it is necessary to recover intermediate atmospheric models by means of accurate techniques that go beyond simple linear interpolation and capture the intricacies of the data. Our goal is to establish a reliable, precise, lightweight, and fast method for recovering stellar model atmospheres, that is to say the stratification of mass column, temperature, gas pressure, and electronic density with optical depth given any combination of the defining atmospheric specific parameters: metallicity, effective temperature, and surface gravity, as well as the abundances of other key chemical elements. We employed a fully connected deep neural network which in turn uses a 1D convolutional auto-encoder to extract the nonlinearities of a grid using the ATLAS9 and MARCS model atmospheres. This new method we call iNNterpol effectively takes into account the nonlinearities in the relationships of the data as opposed to traditional machine-learning methods, such as the light gradient boosting method (LightGBM), that are repeatedly used for their speed in well-known competitions with reduced datasets. We show a higher precision with a convolutional auto-encoder than using principal component analysis as a feature extractor.We believe it constitutes a useful tool for generating fast and precise stellar model atmospheres, mitigating convergence issues, as well as a framework for future developments. The code and data for both training and direct interpolation are available online at https://github.com/cwestend/iNNterpol for full reproducibility and to serve as a practical starting point for other continuous 1D data in the field and elsewhere., Comment: Accepted for publication in Astronomy and Astrophysics, 11 pages, 27 figures, 3 tables

Details of the magnetic field in the quiet Sun chromosphere are key to our understanding of essential aspects of the solar atmosphere. We aim to determine the longitudinal magnetic field component (B_lon) of quiet Sun regions depending on their size. We estimated B_lon by applying the weak-field approximation (WFA) to high-spatial-resolution Ca II 854.2 nm data taken with the Swedish 1m Solar Telescope. Specifically, we analyzed the estimates inferred for different spectral ranges using the data at the original cadence and temporally integrated signals. The longitudinal magnetic field in each considered plasma structure correlates with its size. Using a spectral range restricted to the line core leads to chromospheric longitudinal fields varying from 50 G at the edges to 150-500 G at the center of the structure. These values increase as the spectral range widens due to the photospheric contribution. However, the difference between this contribution and the chromospheric one is not uniform for all structures. Small and medium-sized concentrations show a steeper height gradient in B_lon compared to their chromospheric values, so estimates for wider ranges are less trustworthy. Signal addition does not alleviate this situation as the height gradients in B_lon are consistent with time. Finally, despite the amplified noise levels that deconvolving processes may cause, data restored with the destretching technique show similar results, though are affected by smearing. We obtained B_lon estimates similar to those previously found, except for large concentrations and wide spectral ranges. In addition, we report a correlation between the height variation of B_lon compared to the chromospheric estimates and the concentration size. This correlation affects the difference between the photospheric and chromospheric magnetic flux values and the reliability of the estimates for wider spectral ranges., Comment: 14 pages, 14 figures. Accepted for publication in Astronomy & Astrophysics; abstract has been abridged

Context. Activity on the far side of the Sun is routinely studied through the analysis of the seismic oscillations detected on the near side using helioseismic techniques such as phase shift sensitive holography. Recently, the neural network FarNet was developed to improve these detections. Aims. We aim to create a new machine learning tool, FarNet II, which further increases the scope of FarNet, and to evaluate its performance in comparison to FarNet and the standard helioseismic method for detecting far side activity. Methods. We developed FarNet II, a neural network that retains some of the general characteristics of FarNet but improves the detections in general, as well as the temporal coherence among successive predictions. The main novelties are the implementation of attention and convolutional long short term memory (ConvLSTM) modules. A cross validation approach, training the network 37 times with a different validation set for each run, was employed to leverage the limited amount of data available. We evaluate the performance of FarNet II using three years of extreme ultraviolet observations of the far side of the Sun acquired with the Solar Terrestrial Relations Observatory (STEREO) as a proxy of activity. The results from FarNet II were compared with those obtained from FarNet and the standard helioseismic method using the Dice coefficient as a metric. Results. FarNet II achieves a Dice coefficient that improves that of FarNet by over 0.2 points for every output position on the sequences from the evaluation dates. Its improvement over FarNet is higher than that of FarNet over the standard method. Conclusions. The new network is a very promising tool for improving the detection of activity on the far side of the Sun given by pure helioseismic techniques. Space weather forecasts can potentially benefit from the higher sensitivity provided by this novel method., Comment: Accepted for publication in Astronomy and Astrophysics. Abridged abstract

The CLASP2 (Chromospheric LAyer SpectroPolarimeter 2) sounding rocket mission was launched on 2019 April 11. CLASP2 measured the four Stokes parameters of the Mg II h & k spectral region around 2800 Angstroms along a 200 arcsecond slit at three locations on the solar disk, achieving the first spatially and spectrally resolved observations of the solar polarization in this near ultraviolet region. The focus of the work presented here is the center-to-limb variation of the linear polarization across these resonance lines, which is produced by the scattering of anisotropic radiation in the solar atmosphere. The linear polarization signals of the Mg II h & k lines are sensitive to the magnetic field from the low to the upper chromosphere through the Hanle and magneto-optical effects. We compare the observations to theoretical predictions from radiative transfer calculations in unmagnetized semi-empirical models, arguing that magnetic fields and horizontal inhomogeneities are needed to explain the observed polarization signals and spatial variations. This comparison is an important step in both validating and refining our understanding of the physical origin of these polarization signatures, and also in paving the way toward future space telescopes for probing the magnetic fields of the solar upper atmosphere via ultraviolet spectropolarimetry., Comment: 14 pages, 5 figures, to be published in the Astrophysical Journal (ApJ), initially submitted May 2022, revised submission July 2022

We study the impact of the loss of axial symmetry around the optical axis on the polarimetric properties of a telescope with segmented primary mirror when each segment is present in a different aging stage. The different oxidation stage of each segment as they are substituted in time leads to non-negligible crosstalk terms. This effect is wavelength dependent and it is mainly determined by the properties of the reflecting material. For an aluminum coating, the worst polarimetric behavior due to oxidation is found for the blue part of the visible. Contrarily, dust -- as modeled in this work -- does not significantly change the polarimetric behavior of the optical system . Depending on the telescope, there might be segment substitution sequences that strongly attenuate this instrumental polarization.

The computational cost of fast non-LTE synthesis is one of the challenges that limits the development of 2D and 3D inversion codes. It also makes the interpretation of observations of lines formed in the chromosphere and transition region a slow and computationally costly process, which limits the inference of the physical properties on rather small fields of view. Having access to a fast way of computing the deviation from the LTE regime through the departure coefficients could largely alleviate this problem. We propose to build and train a graph network that quickly predicts the atomic level populations without solving the non-LTE problem. We find an optimal architecture for the graph network for predicting the departure coefficients of the levels of an atom from the physical conditions of a model atmosphere. A suitable dataset with a representative sample of potential model atmospheres is used for training. This dataset has been computed using existing non-LTE synthesis codes. The graph network has been integrated into existing synthesis and inversion codes for the particular case of \caii. We demonstrate orders of magnitude gain in computing speed. We analyze the generalization capabilities of the graph network and demonstrate that it produces good predicted departure coefficients for unseen models. We implement this approach in \hazel\ and show how the inversions nicely compare with those obtained with standard non-LTE inversion codes. Our approximate method opens up the possibility of extracting physical information from the chromosphere on large fields-of-view with time evolution., Comment: 16 pages, 10 figures, Accepted in ApJ on 01/02/2022 after A&A suggested to be published in a more specialized journal

The non-uniform surface temperature distribution of rotating active stars is routinely mapped with the Doppler Imaging technique. Inhomogeneities in the surface produce features in high-resolution spectroscopic observations that shift in wavelength depending on their position on the visible hemisphere. The inversion problem has been systematically solved using maximum a-posteriori regularized methods assuming smoothness or maximum entropy. Our aim in this work is to solve the full Bayesian inference problem, by providing access to the posterior distribution of the surface temperature in the star. We use amortized neural posterior estimation to produce a model that approximates the high-dimensional posterior distribution for spectroscopic observations of selected spectral ranges sampled at arbitrary rotation phases. The posterior distribution is approximated with conditional normalizing flows, which are flexible, tractable and easy to sample approximations to arbitrary distributions. When conditioned on the spectroscopic observations, they provide a very efficient way of obtaining samples from the posterior distribution. The conditioning on observations is obtained through the use of Transformer encoders, which can deal with arbitrary wavelength sampling and rotation phases. Our model can produce thousands of posterior samples per second. Our validation of the model for very high signal-to-noise observations shows that it correctly approximates the posterior, although with some overestimation of the broadening. We apply the model to the moderately fast rotator II Peg, producing the first Bayesian map of its temperature inhomogenities. We conclude that conditional normalizing flows are a very promising tool to carry out approximate Bayesian inference in more complex problems in stellar physics, like constraining the magnetic properties., Comment: 14 pages, 10 figures, submitted to A&A

Stokes inversion techniques are very powerful methods for obtaining information on the thermodynamic and magnetic properties of solar and stellar atmospheres. In recent years, very sophisticated inversion codes have been developed that are now routinely applied to spectro-polarimetric observations. Most of these inversion codes are designed for finding an optimum solution to the nonlinear inverse problem. However, to obtain the location of potentially multimodal cases (ambiguities), the degeneracies, and the uncertainties of each parameter inferred from the inversions, algorithms such as Markov chain Monte Carlo (MCMC), require to evaluate the likelihood of the model thousand of times and are computationally costly. Variational methods are a quick alternative to Monte Carlo methods by approximating the posterior distribution by a parametrized distribution. In this study, we introduce a highly flexible variational inference method to perform fast Bayesian inference, known as normalizing flows. Normalizing flows are a set of invertible, differentiable, and parametric transformations that convert a simple distribution into an approximation of any other complex distribution. If the transformations are conditioned on observations, the normalizing flows can be trained to return Bayesian posterior probability estimates for any observation. We illustrate the ability of the method using a simple Milne-Eddington model and a complex non-LTE inversion. However, the method is extremely general and other more complex forward models can be applied. The training procedure need only be performed once for a given prior parameter space and the resulting network can then generate samples describing the posterior distribution several orders of magnitude faster than existing techniques., Comment: 11 pages, 7 figures, accepted for publication in A&A

Context. Far-side helioseismology is a technique used to infer the presence of active regions in the far hemisphere of the Sun based on the interpretation of oscillations measured in the near hemisphere. A neural network has been recently developed to improve the sensitivity of the seismic maps to the presence of far-side active regions. Aims. Our aim is to evaluate the performance of the new neural network approach and to thoroughly compare it with the standard method commonly applied to predict far-side active regions from seismic measurements. Methods. We have computed the predictions of active regions using the neural network and the standard approach from five years of far-side seismic maps as a function of the selected threshold in the signatures of the detections. The results have been compared with direct extreme ultraviolet observations of the far hemisphere acquired with the Solar Terrestrial Relations Observatory (STEREO). Results. We have confirmed the improved sensitivity of the neural network to the presence of far-side active regions. Approximately 96% of the active regions identified by the standard method with a strength above the threshold commonly employed by previous analyses are related to locations with enhanced extreme ultraviolet emission. For this threshold, the false positive ratio is 3.75%. For an equivalent false positive ratio, the neural network produces 47% more true detections. Weaker active regions can be detected by relaxing the threshold in their seismic signature. Conclusions. The neural network is a promising approach to improve the interpretation of the seismic maps provided by local helioseismic techniques., Comment: Accepted for publication in Astronomy and Astrophysics. Abridged abstract

Routine ultraviolet imaging of the Sun's upper atmosphere shows the spectacular manifestation of solar activity; yet we remain blind to its main driver, the magnetic field. Here we report unprecedented spectropolarimetric observations of an active region plage and its surrounding enhanced network, showing circular polarization in ultraviolet (Mg II $h$ & $k$ and Mn I) and visible (Fe I) lines. We infer the longitudinal magnetic field from the photosphere to the very upper chromosphere. At the top of the plage chromosphere the field strengths reach more than 300 gauss, strongly correlated with the Mg II $k$ line core intensity and the electron pressure. This unique mapping shows how the magnetic field couples the different atmospheric layers and reveals the magnetic origin of the heating in the plage chromosphere., Comment: 50 pages, 11 figures, 1 table, published in Science Advances

While the longitudinal field that dominates photospheric network regions has been studied extensively, small scale transverse fields have recently been found to be ubiquitous in the quiet internetwork photosphere. Few observations have captured how this field evolves. We aim to statistically characterise the magnetic properties and observe the temporal evolution of small scale magnetic features. We present two high spatial/temporal resolution observations that reveal the dynamics of two disk centre internetwork regions taken by the new GRIS/IFU (GREGOR Infrared Spectrograph Integral Field Unit) with the highly magnetically sensitive Fe I line pair at 15648.52 {\AA} and 15652.87 {\AA}. With the SIR code, we consider two inversion schemes: scheme 1 (S1), where a magnetic atmosphere is embedded in a field free medium, and scheme 2 (S2), with two magnetic models and a fixed stray light component. S1 inversions returned a median magnetic field strength of 200 and 240 G for the two datasets, respectively. We consider the median transverse (horizontal) component, among pixels with Stokes Q or U, and the median unsigned longitudinal (vertical) component, among pixels with Stokes V, above a noise threshold. We determined the former to be 263 G and 267 G, and the latter to be 131 G and 145 G, for the two datasets, respectively. We present three regions of interest (ROIs), tracking the dynamics of small scale magnetic features. We apply S1 and S2 inversions to specific profiles, and find S2 produces better approximations when there is evidence of mixed polarities. We find patches of linear polarization with magnetic flux density between 130 and 150 G, appearing preferentially at granule/intergranular lane (IGL) boundaries. The weak hG magnetic field appears to be organised in terms of complex loop structures, with transverse fields often flanked by opposite polarity longitudinal fields., Comment: Accepted for publication in A&A, 22 pages, 17 figures. Abstract abridged for mailing list, full abstract included in PDF

We have developed an inversion procedure designed for high-resolution solar spectro-polarimeters, such as Hinode/SP or DKIST/ViSP. 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 to small-scale magnetic elements, appear cool at the log(tau_500)=-3 and -4 level, and viceversa. Finally, we find hot arcs on the limb side of magnetic pores, which we interpret as the first direct observational evidence of the "hot wall" effect in temperature., Comment: Submitted to Astronomy and Astrophysics. Comments are welcome

Finding potential life harboring exo-Earths 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/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. We leverage the 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. We propose in this work to use methods based on the procedural generation of planets, inspired by what we found on Earth. We also consider mapping the recovery of surfaces and the presence of persistent cloud in cloudy planets. We show that the a 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 one can potentially map the distribution of persistent clouds that always occur on the same position on the surface (associated to orography and sea surface temperatures) together with non-persistent clouds that move across the surface. This will become the first test one can 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 as a strong proxy for truly habitable conditions., Comment: 12 pages, 9 figures, accepted for publication in A&A, code on https://github.com/aasensio/neural_exocartography

Aims. We report the results of a novel determination of the solar oxygen abundance using spatially resolved observations and inversions. We seek to derive the photospheric solar oxygen abundance with a method that is robust against uncertainties in the model atmosphere. Methods. We use observations with spatial resolution obtained at the Vacuum Tower Telescope (VTT) to derive the oxygen abundance at 40 different spatial positions in granules and intergranular lanes. We first obtain a model for each location by inverting the Fe I lines with the NICOLE inversion code. These models are then integrated into a hierarchical Bayesian model that is used to infer the most probable value for the oxygen abundance that is compatible with all the observations. The abundance is derived from the [O I] forbidden line at 6300 {\AA} taking into consideration all possible nuisance parameters that can affect the abundance. Results. Our results show good agreement in the inferred oxygen abundance for all the pixels analyzed, demonstrating the robustness of the analysis against possible systematic errors in the model. We find a slightly higher oxygen abundance in granules than in intergranular lanes when treated separately (log(\epsilon_O) = 8.83 \pm 0.02 vs log(\epsilon_O) = 8.76 \pm 0.02), which is a difference of approximately 2-{\sigma}. This tension suggests that some systematic errors in the model or the radiative transfer still exist but are small. When taking all pixels together, we obtain an oxygen abundance of log(\epsilon_O)=8.80 \pm 0.03, which is compatible with both granules and lanes within 1-{\sigma}. The spread of results is due to both systematic and random errors., Comment: 13 pages, 16 figures, accepted for publication in A&A

We investigate the dynamics and magnetic properties of the plasma, such as line-of-sight velocity (LOS), optical depth, vertical and horizontal magnetic fields, belonging to an erupted solar filament. The filament eruption was observed with the GREGOR Infrared Spectrograph (GRIS) at the 1.5-meter GREGOR telescope on 2016 July 3. Three consecutive full-Stokes slit-spectropolarimetric scans in the He I 10830 \r{A} spectral range were acquired. The Stokes I profiles were classified using the machine learning k-means algorithm and then inverted with different initial conditions using the HAZEL code. The erupting-filament material presents the following physical conditions: (1) ubiquitous upward motions with peak LOS velocities of ~73 km/s; (2) predominant large horizontal components of the magnetic field, on average, in the range of 173-254 G, whereas the vertical components of the fields are much lower, on average between 39-58 G; (3) optical depths in the range of 0.7-1.1. The average azimuth orientation of the field lines between two consecutive raster scans (<2.5 minutes) remained constant. The analyzed filament eruption belonged to the fast rising phase, with total velocities of about 124 km/s. The orientation of the magnetic field lines does not change from one raster scan to the other, indicating that the untwisting phase has not started yet. The untwisting seems to start about 15 min after the beginning of the filament eruption., Comment: Accepted for publication in Astronomy & Astrophysics, 12 pages, 13 figures, 1 appendix, 2 online movies

Observations from ground based telescopes are affected by the presence of the Earth atmosphere, which severely perturbs them. The use of adaptive optics techniques has allowed us to partly beat 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 been recently proposed as an efficient way to accelerate these image reconstructions. Currently, these deep neural networks are trained with supervision, so 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. Our aim here is to propose a general unsupervised training scheme that allows multiframe blind deconvolution deep learning systems to be trained simply with observations. The approach can be applied for the correction of point-like as well as extended objects. Leveraging the linear image formation theory and a probabilistic approach to the blind deconvolution problem produces a physically-motivated loss function. The optimization of this loss function allows an end-to-end training of a machine learning model composed of three neural networks. 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 technniques. 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., Comment: 11 pages, 4 figures, accepted for publication in A&A

Aims. We use high-resolution spectropolarimetric observations in the Ca ii 8542 A line obtained with the SST to study the magnetic field in solar spicules. Methods. The equations that result from the application of the Weak Field Approximation (WFA) to the radiative transfer equations are used to infer the LOS component of the magnetic field (BLOS). Two restrictive conditions are imposed on the Stokes I and V profiles at each pixel before they can be used in a Bayesian inversion to compute its BLOS. Results. The LOS magnetic field component has been inferred in six data sets totalling 448 spectral scans in the Ca ii 8542 A line and containing both active region and quiet Sun areas, with values of hundreds of G being abundantly inferred. There seems to be no difference, from the statistical point of view, between the magnetic field strength of spicules in the quiet Sun or near an active region. On the other hand, the BLOS distributions present smaller values on the disk than off-limb, a fact that can be explained by the effect of superposition on the chromosphere of on-disk structures. We find that spicules in the vicinity of a sunspot have a magnetic field polarity (i.e. north or south) equal to that of the sunspot. This paper also contains an analysis of the effect of off-limb overlapping structures on the observed Stokes I and V parameters and the BLOS obtained from the WFA. It is found that this value is equal to or smaller than the largest LOS magnetic field components of the two structures. In addition, using random BLOS, Doppler velocities and line intensities of these two structures leads in ~ 50% of the cases to Stokes I and V parameters unsuitable to be used with the WFA. Conclusions. Our results present a scarcity of LOS magnetic field components smaller than some 50 G, which must not be taken as evidence against the existence of these magnetic field strengths in spicules.

The nonlinear force-free field (NLFFF) modeling has been extensively used to infer the three-dimensional (3D) magnetic field in the solar corona. One of the assumptions in the NLFFF extrapolation is that the plasma beta is low, but this condition is considered to be incorrect in the photosphere. We examine direct measurements of the chromospheric magnetic field in two active regions through spectropolarimetric observations at He I 10830 A, which are compared with the potential fields and NLFFFs extrapolated from the photosphere. The comparisons allow quantitative estimation of the uncertainty in the NLFFF extrapolation from the photosphere. Our analysis shows that observed chromospheric magnetic field may have larger non-potentiality compared to the photospheric magnetic field. Moreover, the large non-potentiality in the chromospheric height may not be reproduced by the NLFFF extrapolation from the photospheric magnetic field. The magnitude of the underestimation of the non-potentiality at chromospheric heights may reach 30-40 degree in shear signed angle in some locations. This deviation may be caused by the non-force-freeness in the photosphere. Our study suggests the importance of the inclusion of measured chromospheric magnetic fields in the NLFFF modeling for the improvement of the coronal extrapolation., Comment: accepted in ApJ

The analysis of waves in the visible side of the Sun allows the detection of active regions in the farside through local helioseismology techniques. The knowledge of the magnetism in the whole Sun, including the non-visible hemisphere, is fundamental for several space weather forecasting applications. Seismic identification of farside active regions is challenged by the reduced signal-to-noise, and only large and strong active regions can be reliable detected. Here we develop a new methodology to improve the identification of active region signatures in farside seismic maps. We have constructed a deep neural network that associates the farside seismic maps obtained from helioseismic holography with the probability of presence of active regions in the farside. The network has been trained with pairs of helioseismic phase shift maps and Helioseismic and Magnetic Imager magnetograms acquired half a solar rotation later, which were used as a proxy for the presence of active regions in the farside. The method has been validated using a set of artificial data, and it has also been applied to actual solar observations during the period of minimum activity of the solar cycle 24. Our approach shows a higher sensitivity to the presence of farside active regions than standard methods applied up to date. The neural network can significantly increase the number of detected farside active regions, and will potentially improve the application of farside seismology to space weather forecasting., Comment: Accepted for publication in A&A

We analyze high-resolution spectropolarimetric observations of a flux emerging region (FER) in order to understand its magnetic and kinematic structure. Our spectropolarimetric observations in the He I 1083.0 nm spectral region of a FER are recorded with GRIS at the 1.5 m aperture GREGOR telescope. A Milne-Eddington based inversion code was employed to extract the photospheric information of the Si I spectral line, whereas the He I triplet line was analyzed with the Hazel inversion code, which takes into account the joint action of the Hanle and the Zeeman effect. The spectropolarimetric analysis of Si I line displays a complex magnetic structure near the vicinity of FER. Moreover, we find supersonic downflows of 40 km/sec appears near the footpoints of loops connecting two pores of opposite polarity, whereas a strong upflows of 22 km/sec appears near the apex of the loops. Furthermore, non-force-free field extrapolations were performed separately at two layers in order to understand the magnetic field topology of the FER. We determine, using extrapolations from the photosphere and the observed chromospheric magnetic field, that the average formation height of the He triplet line is 2 Mm from the solar surface. The reconstructed loops using photospheric extrapolations along an arch filament system have a maximum height of 10.5 Mm from the solar surface with a foot-points separation of 19 Mm, whereas the loops reconstructed using chromospheric extrapolations are around 8.4 Mm high from the solar surface with a foot-point separation of 16 Mm at the chromospheric height. The magnetic topology in the FER suggests the presence of small-scale loops beneath the large loops. Under suitable conditions, due to magnetic reconnection, these loops can trigger various heating events in the vicinity of the FER., Comment: 14 pages, 13 figure, 1 table, accepted for publication in A&A

The three-dimensional (3-D) shape of a galaxy inevitably is tied to how it has formed and evolved and to its dark matter halo. Local extremely metal-poor galaxies (XMPs; defined as having an average gas-phase metallicity < 0.1 solar) are important objects for understanding galaxy evolution largely because they appear to be caught in the act of accreting gas from the cosmic web, and their 3-D shape may reflect this. Here we report on the 3-D shape of XMPs as inferred from their observed projected minor-to-major axial ratios using a hierarchical Bayesian inference model, which determines the likely shape and orientation of each galaxy while simultaneously inferring the average shape and dispersion. We selected a sample of 149 XMPs and divided it into three sub-samples according to physical size and found that (1) the stellar component of XMPs of all sizes tends to be triaxial, with an intermediate axis approx 0.7 times the longest axis and that (2) smaller XMPs tend to be relatively thicker, with the shortest axis going from approx 0.15 times the longest axis for the large galaxies to approx 0.4 for the small galaxies. We provide the inferred 3-D shape and inclination of the individual XMPs in electronic format. We show that our results for the intermediate axis are not clouded by a selection effect against face-on XMPs. We discuss how an intermediate axis significantly smaller than the longest axis may be produced by several mechanisms, including lopsided gas accretion, non-axisymmetric star formation, or coupling with an elongated dark matter halo. Large relative thickness may reflect slow rotation, stellar feedback, or recent gas accretion., Comment: Accepted for publicaction in ApJ. 15 pages, 9 figures. Table with the shapes of the individual XMP galaxies available

Our aim is to demonstrate the limitations of using a single component model to study the magnetic field of an active region filament. For that we have analyzed the polarimetric signals of the He I 10830 A multiplet acquired with the infrared spectrograph GRIS of the GREGOR telescope (Tenerife, Spain). After a first analysis of the general properties of the filament using Hazel under the assumption of a single component model atmosphere, in this second part we focus our attention on the observed Stokes profiles and the signatures which cannot be explained with this model. We have found an optically thick filament where the blue and the red components have the same sign in the linear polarization as an indication of radiative transfer effects. Moreover, the circular polarization signals inside the filament show the presence of strong magnetic field gradients. We have also shown that even a filament with such high absorption still shows signatures of the circular polarization generated by the magnetic field below the filament. The reason is that the absorption of the spectral line decays very quickly towards the wings, just where the circular polarization has a larger amplitude. In order to separate both contributions, we explore the possibility of a two component model but the inference becomes impossible to overcome as a high number of solutions is compatible with the observations., Comment: 8 pages, 11 figures, accepted for publication in A&A

The determination of the magnetic filed vector in solar filaments is possible by interpreting the Hanle and Zeeman effects in suitable chromospheric spectral lines like those of the He I multiplet at 10830 A. We study the vector magnetic field of an active region filament (NOAA 12087). Spectropolarimetric data of this active region was acquired with the GRIS instrument at the GREGOR telescope and studied simultaneously in the chromosphere with the He I 10830 A multiplet and in the photosphere with the Si I 10827 A line. As it is usual from previous studies, only a single component model is used to infer the magnetic properties of the filament. The results are put into a solar context with the help of the Solar Dynamic Observatory images. Some results clearly point out that a more complex inversion had to be done. Firstly, the Stokes $V$ map of He I does not show any clear signature of the presence of the filament. Secondly, the local azimuth map follows the same pattern than Stokes $V$ as if the polarity of Stokes $V$ were conditioning the inference to very different magnetic field even with similar linear polarization signals. This indication suggests that the Stokes $V$ could be dominated by the below magnetic field coming from the active region, and not, from the filament itself. Those and more evidences will be analyzed in depth and a more complex inversion will be attempted in the second part of this series., Comment: 18 pages, 19 figures, accepted for publication in A&A

We characterize for the first time the torus properties of an ultra-hard X-ray (14-195 keV) volume-limited (DL<40 Mpc) sample of 24 Seyfert (Sy) galaxies (BCS40 sample). The sample was selected from the Swift/BAT nine month catalog. We use high angular resolution nuclear infrared (IR) photometry and N-band spectroscopy, the CLUMPY torus models and a Bayesian tool to characterize the properties of the nuclear dust. In the case of the Sy1s we estimate the accretion disk contribution to the subarcsecond resolution nuclear IR SEDs (~0.4'') which is, on average, 46+-28, 23+-13 and 11+-5% in the J-, H- and K-bands, respectively. This indicates that the accretion disk templates that assume a steep fall for longer wavelengths than 1 micron might underestimate its contribution to the near-IR emission. Using both optical (broad vs narrow lines) and X-ray (unabsorbed vs absorbed) classifications, we compare the global posterior distribution of the torus model parameters. We confirm that Sy2s have larger values of the torus covering factor (CT~0.95) than Sy1s (CT~0.65) in our volume-limited Seyfert sample. These findings are independent of whether we use an optical or X-ray classification. We find that the torus covering factor remains essentially constant within the errors in our luminosity range and there is no clear dependence with the Eddington ratio. Finally, we find tentative evidence that even an ultra hard X-ray selection is missing a significant fraction of highly absorbed type 2 sources with very high covering factor tori., Comment: 28 pages, 14 figures and 14 tables. Monthly Notices of the Royal Astronomical Society, Accepted 2019 April 4. Received 2019 March 14; in original form 2019 January 18, in press

Spectropolarimetric inversions are routinely used in the field of Solar Physics for the extraction of physical information from observations. The application to two-dimensional fields of view often requires the use of supercomputers with parallelized inversion codes. Even in this case, the computing time spent on the process is still very large. Our aim is to develop a new inversion code based on the application of convolutional neural networks that can quickly provide a three-dimensional cube of thermodynamical and magnetic properties from the interpretation of two-dimensional maps of Stokes profiles. We train two different architectures of fully convolutional neural networks. To this end, we use the synthetic Stokes profiles obtained from two snapshots of three-dimensional magneto-hydrodynamic numerical simulations of different structures of the solar atmosphere. We provide an extensive analysis of the new inversion technique, showing that it infers the thermodynamical and magnetic properties with a precision comparable to that of standard inversion techniques. However, it provides several key improvements: our method is around one million times faster, it returns a three-dimensional view of the physical properties of the region of interest in geometrical height, it provides quantities that cannot be obtained otherwise (pressure and Wilson depression) and the inferred properties are decontaminated from the blurring effect of instrumental point spread functions for free. The code is provided for free on a specific repository, with options for training and evaluation., Comment: 18 pages, 14 figures, accepted for publication in Astronomy & Astrophysics

A classic application of coronal seismology uses transverse oscillations of waveguides to obtain estimates of the magnetic field strength. The procedure requires information on the density of the structures. Often, it ignores the damping of the oscillations. We computed marginal posteriors for parameters such as the waveguide density; the density contrast; the transverse inhomogeneity length-scale; and the magnetic field strength, under the assumption that the oscillations can be modelled as standing magnetohydrodynamic (MHD) kink modes damped by resonant absorption. Our results show that the magnetic field strength can be properly inferred, even if the densities inside and outside the structure are largely unknown. Incorporating observational estimates of plasma density further constrains the obtained posteriors. The amount of information one is willing to include (a priori) for the density and the density contrast influences their corresponding posteriors, but very little the inferred magnetic field strength. The decision to include or leave out the information on the damping and the damping time-scales have a minimal impact on the obtained magnetic field strength. In contrast to the classic method which provides with numerical estimates with error bars or possible ranges of variation for the magnetic field strength, Bayesian methods offer the full distribution of plausibility over the considered range of possible values. The methods are applied to available datasets of observed transverse loop oscillations, can be extended to prominence fine structures or chromospheric spicules and implemented to propagating waves in addition to standing oscillations., Comment: 16 pages, A&A accepted

In this study we explore the diagnostic potential of the chromospheric Ca II line at 8542A for studying the magnetic and dynamic properties of solar filaments. We have acquired high spatial resolution spectropolarimetric observations in the Ca II 8542A line using the CRISP instrument at the Swedish 1-m Solar Telescope. We use the NICOLE inversion code to infer physical properties from observations of a solar filament. We discuss the validity of the results due to the assumption of hydrostatic equilibrium. We have used observations from other telescopes such as CHROTEL and SDO, in order to study large scale dynamics and the long term evolution of the filament. We show that the Ca II 8542A line encodes information of the temperature, line-of-sight velocity and magnetic field vector from the region where the filament is located. The current noise level only allow us to estimate an upper limit of 260G for the total magnetic field of the filament. Our study also reveals that if we only consider information from the aforementioned spectral line, the geometric height, the temperature and the density can be degenerated parameters outside the hydrostatic equilibrium approach., Comment: 14 pages, 13 figures, accepted for publication in A&A

Boyajian's star is an apparently normal main sequence F-type star with a very unusual light curve. The dipping activity of the star, discovered during the Kepler mission, presents deep, asymmetric, and aperiodic events. Here we present high resolution spectroscopic follow-up during some dimming events recorded post-Kepler observations, from ground-based telescopes. We analise data from the HERMES, HARPS-N and FIES spectrographs to characterise the stellar atmosphere and to put some constraints on the hypotheses that have appeared in the literature concerning the occulting elements. The star's magnetism, if existing, is not extreme. The spots on the surface, if present, would occupy 0.02% of the area, at most. The chromosphere, irrespective of the epoch of observation, is hotter than the values expected from radiative equilibrium, meaning that the star has some degree of activity. We find no clear evidence of the interstellar medium nor exocoments being responsible for the dimmings of the light curve. However, we detect at 1-2 sigma level, a decrease of the radial velocity of the star during the first dip recorded after the \emph{\emph{Kepler}} observations. We claim the presence of an optically thick object with likely inclined and high impact parameter orbits that produces the observed Rossiter-McLaughlin effect., Comment: submitted to MNRAS

The Chromospheric Lyman-Alpha Spectro-Polarimeter (CLASP) is a suborbital rocket experiment that on 3rd September 2015 measured the linear polarization produced by scattering processes in the hydrogen Ly-$\alpha$ line of the solar disk radiation, whose line-center photons stem from the chromosphere-corona transition region (TR). These unprecedented spectropolarimetric observations revealed an interesting surprise, namely that there is practically no center-to-limb variation (CLV) in the $Q/I$ line-center signals. Using an analytical model, we first show that the geometrical complexity of the corrugated surface that delineates the TR has a crucial impact on the CLV of the $Q/I$ and $U/I$ line-center signals. Secondly, we introduce a statistical description of the solar atmosphere based on a three-dimensional (3D) model derived from a state-of-the-art radiation magneto-hydrodynamic simulation. Each realization of the statistical ensemble is a 3D model characterized by a given degree of magnetization and corrugation of the TR, and for each such realization we solve the full 3D radiative transfer problem taking into account the impact of the CLASP instrument degradation on the calculated polarization signals. Finally, we apply the statistical inference method presented in a previous paper to show that the TR of the 3D model that produces the best agreement with the CLASP observations has a relatively weak magnetic field and a relatively high degree of corrugation. We emphasize that a suitable way to validate or refute numerical models of the upper solar chromosphere is by confronting calculations and observations of the scattering polarization in ultraviolet lines sensitive to the Hanle effect., Comment: Accepted for publication in The Astrophysical Journal Letters

On 3rd September 2015, the Chromospheric Lyman-Alpha SpectroPolarimeter (CLASP) successfully measured the linear polarization produced by scattering processes in the hydrogen Lyman-$\alpha$ line of the solar disk radiation, revealing conspicuous spatial variations in the $Q/I$ and $U/I$ signals. Via the Hanle effect the line-center $Q/I$ and $U/I$ amplitudes encode information on the magnetic field of the chromosphere-corona transition region (TR), but they are also sensitive to the three-dimensional structure of this corrugated interface region. With the help of a simple line formation model, here we propose a statistical inference method for interpreting the Lyman-$\alpha$ line-center polarization observed by CLASP., Comment: Accepted for publication in The Astrophysical Journal

The quality of images of the Sun obtained from the ground are severely limited by the perturbing effect of the turbulent Earth's atmosphere. The post-facto correction of the images to compensate for the presence of the atmosphere require the combination of high-order adaptive optics techniques, fast measurements to freeze the turbulent atmosphere and very time consuming blind deconvolution algorithms. Under mild seeing conditions, blind deconvolution algorithms can produce images of astonishing quality. They can be very competitive with those obtained from space, with the huge advantage of the flexibility of the instrumentation thanks to the direct access to the telescope. In this contribution we leverage deep learning techniques to significantly accelerate the blind deconvolution process and produce corrected images at a peak rate of ~100 images per second. We present two different architectures that produce excellent image corrections with noise suppression while maintaining the photometric properties of the images. As a consequence, polarimetric signals can be obtained with standard polarimetric modulation without any significant artifact. With the expected improvements in computer hardware and algorithms, we anticipate that on-site real-time correction of solar images will be possible in the near future., Comment: 16 pages, 12 figures, accepted for publication in A&A

We present MOLPOP-CEP, a universal line transfer code that allows the exact calculation of multi-level line emission from a slab with variable physical conditions for any arbitrary atom or molecule for which atomic data exist. The code includes error control to achieve any desired level of accuracy, providing full confidence in its results. Publicly available, MOLPOP-CEP employs our recently developed Coupled Escape Probability (CEP) technique, whose performance exceeds other exact methods by orders of magnitude. The program also offers the option of an approximate solution with different variants of the familiar escape probability method. As an illustration of the MOLPOP-CEP capabilities we present an exact calculation of the Spectral Line Energy Distribution (SLED) of the CO molecule and compare it with escape probability results. We find that the popular large-velocity gradient (LVG) approximation is unreliable at large CO column densities. Providing a solution of the multi-level line transfer problem at any prescribed level of accuracy, MOLPOP-CEP is removing any doubts about the validity of its final results., Comment: 10 pages, 3 figures, accepted for publication in A&A

We present high-precision spectro-polarimetric data with high spatial resolution (0.4$''$) of the very quiet Sun at 1.56$\mu$m obtained with the GREGOR telescope to shed some light on this complex magnetism. Half of our observed quiet-Sun region is better explained by magnetic substructure within the resolution element. However, we cannot distinguish whether this substructure comes from gradients of the physical parameters along the line of sight or from horizontal gradients (across the surface). In these pixels, a model with two magnetic components is preferred, and we find two distinct magnetic field populations. The population with the larger filling factor has very weak ($\sim$150 G) horizontal fields similar to those obtained in previous works. We demonstrate that the field vector of this population is not constrained by the observations, given the spatial resolution and polarimetric accuracy of our data. The topology of the other component with the smaller filling factor is constrained by the observations for field strengths above 250 G: we infer hG fields with inclinations and azimuth values compatible with an isotropic distribution. The filling factors are typically below 30\%. We also find that the flux of the two polarities is not balanced. From the other half of the observed quiet-Sun area $\sim$50\% are two-lobed Stokes $V$ profiles, meaning that 23\% of the field of view can be adequately explained with a single constant magnetic field embedded in a non-magnetic atmosphere. The magnetic field vector and filling factor are reliable inferred in only 50\% based on the regular profiles. Therefore, 12\% of the field of view harbour hG fields with filling factors typically below 30\%. At our present spatial resolution, 70\% of the pixels apparently are non-magnetised., Comment: published in A&A

Chromospheric observations of sunspot umbrae offer an exceptional view of magneto-acoustic shock phenomena and the impact they have on the surrounding magnetically-dominated plasma. We employ simultaneous slit-based spectro-polarimetry and spectral imaging observations of the chromospheric He I 10830{\AA} and Ca II 8542{\AA} lines to examine fluctuations in the umbral magnetic field caused by the steepening of magneto-acoustic waves into umbral flashes. Following the application of modern inversion routines, we find evidence to support the scenario that umbral shock events cause expansion of the embedded magnetic field lines due to the increased adiabatic pressure. The large number statistics employed allow us to calculate the adiabatic index, gamma = 1.12 +/- 0.01, for chromospheric umbral locations. Examination of the vector magnetic field fluctuations perpendicular to the solar normal revealed changes up to ~200 G at the locations of umbral flashes. Such transversal magnetic field fluctuations have not been described before. Through comparisons with non-linear force-free field extrapolations, we find that the perturbations of the transverse field components are orientated in the same direction as the quiescent field geometries. This implies that magnetic field enhancements produced by umbral flashes are directed along the motion path of the developing shock, hence producing relatively small changes, up to a maximum of ~8 degrees, in the inclination and/or azimuthal directions of the magnetic field. Importantly, this work highlights that umbral flashes are able to modify the full vector magnetic field, with the detection of the weaker transverse magnetic field components made possible by high-resolution data combined with modern inversion routines., Comment: 16 pages, 7 figures, accepted for publication in The Astrophysical Journal

We present a photometric detection of the first brightness dips of the unique variable star KIC 8462852 since the end of the Kepler space mission in 2013 May. Our regular photometric surveillance started in October 2015, and a sequence of dipping began in 2017 May continuing on through the end of 2017, when the star was no longer visible from Earth. We distinguish four main 1-2.5% dips, named "Elsie," "Celeste," "Skara Brae," and "Angkor", which persist on timescales from several days to weeks. Our main results so far are: (i) there are no apparent changes of the stellar spectrum or polarization during the dips; (ii) the multiband photometry of the dips shows differential reddening favoring non-grey extinction. Therefore, our data are inconsistent with dip models that invoke optically thick material, but rather they are in-line with predictions for an occulter consisting primarily of ordinary dust, where much of the material must be optically thin with a size scale <<1um, and may also be consistent with models invoking variations intrinsic to the stellar photosphere. Notably, our data do not place constraints on the color of the longer-term "secular" dimming, which may be caused by independent processes, or probe different regimes of a single process., Comment: 19 pages, 8 figures, accepted for publication in ApJL

We investigate the properties of a sunspot light-bridge, focusing on the changes produced by the impact of a plasma blob ejected from a C-class flare. We observed a sunspot in active region NOAA 12544 using spectropolarimetric raster maps of the four Fe I lines around 15655 \AA\ with the GREGOR Infrared Spectrograph (GRIS), narrow-band intensity images sampling the Fe I 6173 \AA\ line with the GREGOR Fabry-P\'erot Interferometer (GFPI), and intensity broad band images in G-band and Ca II H band with the High-resolution Fast Imager (HiFI). All these instruments are located at the GREGOR telescope at the Observatorio del Teide, Tenerife, Spain. The data cover the time before, during, and after the flare event. The analysis is complemented with Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI) data from the Solar Dynamics Observatory (SDO). The physical parameters of the atmosphere at differents heights were inferred using spectral-line inversion techniques. We identify photospheric and chromospheric brightenings, heating events, and changes in the Stokes profiles associated to the flare eruption and the subsequent arrival of the plasma blob to the light bridge, after traveling along an active region loop. The measurements suggest that these phenomena are the result of reconnection events driven by the interaction of the plasma blob with the magnetic field topology of the light bridge., Comment: Accepted for publication in A&A

The Helioseismic and Magnetic Imager (HMI) provides continuum images and magnetograms with a cadence better than one per minute. It has been continuously observing the Sun 24 hours a day for the past 7 years. The obvious trade-off between full disk observations and spatial resolution makes HMI not enough to analyze the smallest-scale events in the solar atmosphere. Our aim is to develop a new method to enhance HMI data, simultaneously deconvolving and super-resolving images and magnetograms. The resulting images will mimic observations with a diffraction-limited telescope twice the diameter of HMI. Our method, which we call Enhance, is based on two deep fully convolutional neural networks that input patches of HMI observations and output deconvolved and super-resolved data. The neural networks are trained on synthetic data obtained from simulations of the emergence of solar active regions. We have obtained deconvolved and supper-resolved HMI images. To solve this ill-defined problem with infinite solutions we have used a neural network approach to add prior information from the simulations. We test Enhance against Hinode data that has been degraded to a 28 cm diameter telescope showing very good consistency. The code is open source., Comment: 13 pages, 10 figures. Accepted for publication in Astronomy & Astrophysics

$Context$. The thermal structure of the penumbra below its visible surface (i.e., $\tau_5 \ge 1$) has important implications for our present understanding of sunspots and their penumbrae: their brightness and energy transport, mode conversion of magneto-acoustic waves, sunspot seismology, and so forth. $Aims$. We aim at determining the thermal stratification in the layers immediately beneath the visible surface of the penumbra: $\tau_5 \in [1,3]$ ($\approx 70-80$ km below the visible continuum-forming layer). $Methods$. We analyzed spectropolarimetric data (i.e., Stokes profiles) in three Fe \textsc{i} lines located at 1565 nm observed with the GRIS instrument attached to the 1.5-meter solar telescope GREGOR. The data are corrected for the smearing effects of wide-angle scattered light and then subjected to an inversion code for the radiative transfer equation in order to retrieve, among others, the temperature as a function of optical depth $T(\tau_5)$. $Results$. We find that the temperature gradient below the visible surface of the penumbra is smaller than in the quiet Sun. This implies that in the region $\tau_5 \ge 1$ the penumbral temperature diverges from that of the quiet Sun. The same result is obtained when focusing only on the thermal structure below the surface of bright penumbral filaments. We interpret these results as evidence of a thick penumbra, whereby the magnetopause is not located near its visible surface. In addition, we find that the temperature gradient in bright penumbral filaments is lower than in granules. This can be explained in terms of the limited expansion of a hot upflow inside a penumbral filament relative to a granular upflow, as magnetic pressure and tension forces from the surrounding penumbral magnetic field hinder an expansion like this., Comment: 5 pages; 2 figures; accepted for publication in Astronomy and Astrophysics Letters

There is a thin transition region (TR) in the solar atmosphere where the temperature rises from 10,000 K in the chromosphere to millions of degrees in the corona. Little is known about the mechanisms that dominate this enigmatic region other than the magnetic field plays a key role. The magnetism of the TR can only be detected by polarimetric measurements of a few ultraviolet (UV) spectral lines, the Lyman-$\alpha$ line of neutral hydrogen at 121.6 nm (the strongest line of the solar UV spectrum) being of particular interest given its sensitivity to the Hanle effect (the magnetic-field-induced modification of the scattering line polarization). We report the discovery of linear polarization produced by scattering processes in the Lyman-$\alpha$ line, obtained with the Chromospheric Lyman-Alpha Spectro-Polarimeter (CLASP) rocket experiment. The Stokes profiles observed by CLASP in quiet regions of the solar disk show that the $Q/I$ and $U/I$ linear polarization signals are of the order of 0.1 % in the line core and up to a few percent in the nearby wings, and that both have conspicuous spatial variations with scales of $\sim 10$ arcsec. These observations help constrain theoretical models of the chromosphere-corona TR and extrapolations of the magnetic field from photospheric magnetograms. In fact, the observed spatial variation from disk to limb of polarization at the line core and wings already challenge the predictions from three-dimensional magnetohydrodynamical models of the upper solar chromosphere.

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 and at 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., Comment: 9 pages, 5 figures, accepted for publication in A&A

We present mid-infrared (MIR, 7.5-13.5 $\mu$m) imaging and spectroscopy observations obtained with the CanariCam (CC) instrument on the 10.4m Gran Telescopio CANARIAS for a sample of 20 nearby, MIR bright and X-ray luminous QSOs. We find that for the majority of QSOs the MIR emission is unresolved at angular scales nearly 0.3 arcsec, corresponding to physical scales $<600$ pc. We find that the higher-spatial resolution CC spectra have similar shapes to those obtained with Spitzer/IRS, and hence we can assume that the spectra are not heavily contaminated by extended emission in the host galaxy. We thus take advantage of the higher signal to noise Spitzer/IRS spectra, as a fair representation of the nuclear emission, to decompose it into a combination of active galactic nuclei (AGN), polycyclic aromatic hydrocarbon (PAH) and stellar components. In most cases the AGN is the dominant component, with a median contribution of 85 per cent of the continuum light at MIR (5-15 $\mu$m) within the IRS slit. This IR AGN emission is well reproduced by clumpy torus models. We find evidence for significant differences in the parameters that describe the dusty tori of QSOs when compared with the same parameters of Seyfert 1 and 2 nuclei. In particular, we find a lower number of clouds ($N_{0}<12$), steeper radial distribution of clouds ($q=1.5-3.0$), and clouds that are less optically thick ($\tau_{V}<100$) than in Seyfert 1, which could be attributed to dusty structures that have been partially evaporated and piled up by the higher radiation field in QSOs. We find that the combination of the angular width $\sigma_{torus}$, viewing angle $i$, and number of clouds along the equatorial line $N_{0}$, produces large escape probabilities ($P_{esc} > 2$ per cent) and low geometrical covering factors ($f_{2}<0.6$), as expected for AGN with broad lines in their optical spectra., Comment: Accepted for publication in MNRAS