Multi-wavelength observations become very popular in astronomy. Even though there are some correlations among different sensor images, it is not easy to translate from one to the other one. In this talk, we apply a deep learning method for image-to-image translation, based on conditional generative adversarial networks (cGANs), to astronomical images. To examine the validity of the method for scientific data, we consider several different types of pairs: (1) Generation of stack images from single SDSS images, (2) Generation of SDO/EUV images from SDO/HMI magnetograms, (3) Generation of farside magnetograms from STEREO/EUVI images, (4) Generation of EUV & X-ray images from Carrington sunspot drawing, and (5) Generation of solar magnetograms from Ca II images. It is very impressive that AI-generated ones are quite consistent with actual ones. We will discuss several scientific application of such an image translation method scuh as the sunspot evolution from backside to frontside. In addition, we apply the convolution neural network to the forecast of solar flares and find that our method is better than the conventional method. Our study also shows that the forecast of solar proton flux profiles using Long and Short Term Memory method is better than the autoregressive method. We will discuss several applications of these methodologies for scientific research.
With unprecedented angular resolution of 0.02" of ALMA, we have resolved the disk and jet system in the young protostellar system HH 212 in Orion. The disk is resolved for the first time in the vertical direction, showing a dark lane sandwiched between two bright features, appearing as a hamburger. The disk is flared as expected for an accretion disk. A highly collimated jet is also detected ejecting from the disk, consisting of a train of fast-moving bullets. It is resolved, showing a rotation across the jet axis. Thus, our ALMA observations show that a hungry baby star is spitting a chain of spinning bullets when eating a space hamburger. I will also report the detection of prebiotic complex organic molecules in the disk atmosphere and discuss their possible formation on icy grains.
First of all, I shall give an overview on the research status about stellar magnetic activity and exoplanetary systems at Yunnan Observatories. Then, some more detailed results derived during recent years will be introduced. For stellar activity, I shall talk about the study on chromospheric and photospheric activities by using high-resolution spectroscopy, magnetic field by using Zeeman Doppler imaging method. For exoplanetary systems, I shall talk about the study on wide field transit survey project, TTV follow-up observations. Finally, the prospects in the near future will be given.
The ground-based photometric observations of asteroids still is the main source to understand their basic physical properties, even though some space mission and space-based instruments have been applied in physical studies of asteroids. With the help of developments on scattering theories and 3D shape models of asteroid, more and more asteroids are studies their basic physical parameters of asteroids from the photometric data. In this presentation, I will present photometric studies for some selected asteroids. In detail, they are: (1)To determine photometric phase functions of asteroids (107) Camilla and (106) Dione assuming an ellipsoid shape and a cellinoid shape respectively; and (2) To inverse convex shape of main-belt slow rotating asteroids (168) Sibylla and (346)Hermentaria and a near Earth asteroid (3200) Phaethon. Based on derived photometric phase functions, the geometric albedo, and even rough taxonomic classification of asteroids are inferred. With the virtual photometry Monta Carlo method, the uncertainties of spin parameters of selected asteroids were compared.