Stokes Inversion based on Convolutional Neural Networks

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 & Astrophysic

Enhancing SDO/HMI images using deep learning

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 & Astrophysic

Labyrinthine artery aneurysm as an internal auditory canal mass.

We present the first case report of a labyrinthine artery aneurysm masquerading as an internal auditory canal tumor. A 72-year-old woman presented with sudden onset right facial paralysis, facial pain, hearing loss, and vertigo. She demonstrated dense right-sided facial paralysis involving all branches of the facial nerve, left beating horizontal nystagmus, and anacusis of the right ear. Magnetic resonance imaging with contrast demonstrated a 6 × 7 mm peripherally enhancing lesion with lack of central uptake filling the right internal auditory canal. The patient elected to proceed with translabyrinthine approach to the internal auditory canal for definitive resection of the mass as well as to decompress the neural structures of the internal auditory canal in an attempt to recover neural function, particularly of the facial nerve. Intraoperatively, the internal auditory canal mass was resected with minimal difficulty, with intraoperative dissection notable for brisk bleeding at the medial base of the tumor just as the tumor was dissected off its medial fibrous attachments. Final pathology of the resected mass revealed a blood vessel with mucinous degeneration of the medial layer of the vessel wall, with immunohistochemical staining confirming the presence and structure of aneurysmal blood vessel

Series of broad resonances in atomic three-body systems

We re-examine the series of resonances found earlier in atomic three-body systems by solving the Faddeev-Merkuriev integral equations. These resonances are rather broad and line-up at each threshold with gradually increasing gaps, the same way for all thresholds and irrespective of the spatial symmetry. We relate these resonances to the Gailitis mechanism, which is a consequence of the polarization potential.Comment: 14 pages, 7 figures. arXiv admin note: text overlap with arXiv:0810.303

Search for brown-dwarf like secondaries in cataclysmic variables II

We have examined VTL/ISAAC 1-2.5 \umum spectroscopy of a sample of short orbital period cataclysmic variables which are candidates for harboring substellar companions. We provide descriptions of the infrared spectrum of \hbox{EI Psc}, \hbox{V834 Cen}, \hbox{WX Cet}, \hbox{VW Hyi}, \hbox{TY PsA} and \hbox{BW Scl}. Fitting of the IR spectral energy distribution (SED) was performed by comparing the observed spectrum with late-type templates. Absorption features of the secondary star were detected in \hbox{EI Psc} and \hbox{V834 Cen}, consistent with dwarf secondaries of spectral type K 5 $\pm$ 1 and M 8 $\pm$ 0.5, respectively. In addition, we report the first detection of the secondary star in \hbox{VW Hyi}. The SED in this case is well matched by an L 0 $\pm$ 2 type secondary contributing 23 per cent to the overall flux at $\lambda$ = 1.15 \umum. This is a surprising result for a system with a relatively high mass transfer rate. We discuss the implication of our findings on the current scenarios for cataclysmic variable star evolution.Comment: accepted for publication in MNRA