520 Astronomie und zugeordnete Wissenschaften
Refine
Language
- English (3)
Is part of the Bibliography
- yes (3)
Keywords
- techniques: image processing (3) (remove)
Aims. The giant solar filament was visible on the solar surface from 2011 November 8-23. Multiwavelength data from the Solar Dynamics Observatory (SDO) were used to examine counter-streaming flows within the spine of the filament. Methods. We use data from two SDO instruments, the Atmospheric Imaging Assembly (AIA) and the Helioseismic and Magnetic Imager (HMI), covering the whole filament, which stretched over more than half a solar diameter. H alpha images from the Kanzelhohe Solar Observatory (KSO) provide context information of where the spine of the filament is defined and the barbs are located. We apply local correlation tracking (LCT) to a two-hour time series on 2011 November 16 of the AIA images to derive horizontal flow velocities of the filament. To enhance the contrast of the AIA images, noise adaptive fuzzy equalization (NAFE) is employed, which allows us to identify and quantify counter-streaming flows in the filament. We observe the same cool filament plasma in absorption in both H alpha and EUV images. Hence, the counter-streaming flows are directly related to this filament material in the spine. In addition, we use directional flow maps to highlight the counter-streaming flows. Results. We detect counter-streaming flows in the filament, which are visible in the time-lapse movies in all four examined AIA wavelength bands (lambda 171 angstrom, lambda 193 angstrom, lambda 304 angstrom, and lambda 211 angstrom). In the time-lapse movies we see that these persistent flows lasted for at least two hours, although they became less prominent towards the end of the time series. Furthermore, by applying LCT to the images we clearly determine counter-streaming flows in time series of lambda 171 angstrom and lambda 193 angstrom images. In the lambda 304 angstrom wavelength band, we only see minor indications for counter-streaming flows with LCT, while in the lambda 211 angstrom wavelength band the counter-streaming flows are not detectable with this method. The diverse morphology of the filament in H alpha and EUV images is caused by different absorption processes, i.e., spectral line absorption and absorption by hydrogen and helium continua, respectively. The horizontal flows reach mean flow speeds of about 0.5 km s(-1) for all wavelength bands. The highest horizontal flow speeds are identified in the lambda 171 angstrom band with flow speeds of up to 2.5 km s(-1). The results are averaged over a time series of 90 minutes. Because the LCT sampling window has finite width, a spatial degradation cannot be avoided leading to lower estimates of the flow velocities as compared to feature tracking or Doppler measurements. The counter-streaming flows cover about 15-20% of the whole area of the EUV filament channel and are located in the central part of the spine. Conclusions. Compared to the ground-based observations, the absence of seeing effects in AIA observations reveal counter-streaming flows in the filament even with a moderate image scale of 0 '.6 pixel(-1). Using a contrast enhancement technique, these flows can be detected and quantified with LCT in different wavelengths. We confirm the omnipresence of counter-streaming flows also in giant quiet-Sun filaments.
Much of our knowledge about the solar dynamo is based on sunspot observations. It is thus desirable to extend the set of positional and morphological data of sunspots into the past. Gustav Spörer observed in Germany from Anklam (1861–1873) and Potsdam (1874–1894). He left detailed prints of sunspot groups, which we digitized and processed to mitigate artifacts left in the print by the passage of time. After careful geometrical correction, the sunspot data are now available as synoptic charts for almost 450 solar rotation periods. Individual sunspot positions can thus be precisely determined and spot areas can be accurately measured using morphological image processing techniques. These methods also allow us to determine tilt angles of active regions (Joy’s law) and to assess the complexity of an active region.
Most image restoration methods in astronomy rely upon probabilistic tools that infer the best solution for a deconvolution problem. They achieve good performances when the point spread function (PSF) is spatially invariant in the image plane.
However, this condition is not always satisfied in real optical systems. We propose a new method for the restoration of images affected by static and anisotropic aberrations using Deep Neural Networks that can be directly applied to sky images.
The network is trained using simulated sky images corresponding to the T80-S Telescope optical model, a 80-cm survey imager at Cerro Tololo (Chile), which are synthesized using a Zernike polynomial representation of the optical system.
Once trained, the network can be used directly on sky images, outputting a corrected version of the image that has a constant and known PSF across its field of view. The method is to be tested on the T80-S Telescope.
We present the method and results on synthetic data.