Abstract:
Recently, complex horizontal patterns of umbral oscillations have been reported, but their physical nature and origin are still not fully understood. Here we show that the two-dimensional patterns of umbral oscillations of slow waves are inherited from the subphotospheric fast-body modes. Using a simple analytic model, we successfully reproduced the temporal evolution of oscillation patterns with a finite number of fast-body modes. In this model, the radial apparent propagation of the pattern is associated with the appropriate combination of the amplitudes in radial modes. We also find that the oscillation patterns are dependent on the oscillation period. This result indicates that there is a cutoff radial mode, which is a unique characteristic of the model of fast-body modes. In principle, both internal and external sources can excite these fast-body modes and produce horizontal patterns of umbral oscillations.

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Abstract:
We report a magnetic relaxation process inside a sunspot associated with the evolution of a transient light bridge (LB). From high-resolution imaging and spectro-polarimetric data taken by the 1.6 m Goode Solar Telescope installed at Big Bear Solar Observatory, we observe the evolutionary process of a rapidly evolving LB. The LB is formed as a result of the strong intrusion of filamentary structures with relatively horizontal fields into the vertical umbral field region. A strong current density is detected along a localized region where the magnetic field topology changes rapidly in the sunspot, especially in the boundary region between the LB and the umbra, and bright jets are observed intermittently and repeatedly in the chromosphere along this region through magnetic reconnection. In the second half of our observation, the horizontal component of the magnetic field diminishes within the LB, and the typical convection structure within the sunspot, which manifests itself as umbral dots, is restored. Our findings provide a comprehensive perspective not only on the evolution of an LB itself but also on its impacts in the neighboring regions, including the chromospheric activity and the change of magnetic energy of a sunspot.

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Abstract:
A multilayer spectral inversion (MLSI) model has recently been proposed for inferring the physical parameters of plasmas in the solar chromosphere from strong absorption lines taken by the Fast Imaging Solar Spectrograph (FISS). We apply a deep neural network (DNN) technique in order to produce the MLSI outputs with reduced computational costs. We train the model using two absorption lines, Hα and Ca II 8542 Å, taken by FISS, and 13 physical parameters obtained from the application of MLSI to 49 raster scans (~2,000,000 spectra). We use a fully connected network with skip connections and multi-branch architecture to avoid the problem of vanishing gradients and to improve the model's performance. Our test shows that the DNN successfully reproduces the physical parameters for each line with high accuracy and a computing time of about 0.3-0.4 ms per line, which is about 250 times faster than the direct application of MLSI. We also confirm that the DNN reliably reproduces the temporal variations of the physical parameters generated by the MLSI inversion. By taking advantage of the high performance of the DNN, we plan to provide physical parameter maps for all the FISS observations, in order to understand the chromospheric plasma conditions in various solar features.

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