• Journal of The Korean Astronomical Society
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• v.29 no.spc1
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• pp.287-289
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• 1996

IRIS(International Research on the Interior of the sun) is the name of a worldwide network of 6 stations for whole disc Doppler shift measurements. The network has been operating since 1987 and by now a few series of a hundred days long unbroken (by day/night periodicity) data were received. Analysis of these data allowed to receivesome new results which are discussed in the paper.

• Journal of Astronomy and Space Sciences
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• v.26 no.3
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• pp.287-294
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• 2009

In helioseismology it is normally assumed that p-mode oscillations are excited in a statistically independent fashion. Unfortunately, however, this issue is not clearly settled down in that two experiments exist, which apparently look in discrepancy. That is, Appourchaux et al. (2000) looked at bin-to-bin correlation and found no evidence that the assumption is invalid. On the other hand, Roth (2001) reported that p-mode pairs with nearby frequencies tend to be anti-correlated, possibly by a mode-coupling effect. This work is motivated by an idea that one may test if there exists an excess of anticorrelated power variations of pairs of solar p-modes. We have analyzed a 72-day MDI spherical-harmonic time series to examine temporal variations of p-mode power and their correlation. The power variation is computed by a running-window method after the previous study by Roth (2001), and then distribution function of power correlation between mode pairs is produced. We have confirmed Roth's result that there is an excess of anti-correlated p-mode pairs with nearby frequencies. On the other hand, the amount of excess was somewhat smaller than the previous study. Moreover, the distribution function does not exhibit significant change when we paired modes with non-nearby frequencies, implying that the excess is not due to mode coupling. We conclude that the origin of this excess of anticorrelations may not be a solar physical process, by pointing out the possibility of statistical bias playing the central role in producing the excess.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.86.2-86.2
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• 2012

We present the study of association between sub-photospheric flow and photospheric magnetic fields of active regions respectively derived from the local helioseismology and observed magnetic fields. It is believed that the energetic transients, e.g., flares and CMES, are caused by changes in magnetic and velocity field topologies in solar atmosphere. These changes are essentially brought about by the magnetic fields that are rooted beneath the photosphere where they interact and get affected by sub-photospheric flows. Therefore, we expect the topology of sub-surface flows to be correlated with the observable topology of magnetic fields at the photosphere and higher layers. In order to examine the correlation, if any, we computed the near photospheric flows and photospheric magnetic fields using the Doppler velocity and magnetic fields observations, respectively, provided by the SDO/HMI. The high resolution Doppler observations from the HMI enabled us to compute the very high p-modes parameters which sample the sub-photosphere shallow near the photosphere. Furthermore, we compute the sub-photospheric flow topology parameters, e.g., vorticity, kinetic helicity, and photospheric magnetic field topology parameters, e.g., magnetic helicity, from the magnetic fields observations to compare their associations. We present the result of the analysis in the paper.

• Journal of The Korean Astronomical Society
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• v.36 no.2
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• pp.67-72
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• 2003

The basic objective of helioseismology is to determine the structure and the dynamics of the Sun by analysing the frequency spectrum of the solar oscillations. Accurate frequency measurements provide information that enables us to probe the solar interior structure and the dynamics. Therefore the frequency of the solar oscillation is the most fundamental and important information to be extracted from the solar oscillation observation. This is why many efforts have been put into the development of accurate data analysis techniques, as well as observational efforts. To test one's data analysis method, a realistic artificial data set is essential because the newly suggested method is calibrated with a set of artificial data with predetermined parameters. Therefore, unless test data sets reflect the real solar oscillation data correctly, such a calibration is likely incomplete and a unwanted systematic bias may result in. Unfortunately, however, commonly used artificial data generation algorithms insufficiently accommodate physical properties of the stochastic excitation mechanism. One of reason for this is that it is computaionally very expensive to solve the governing equation directly. In this paper we discuss the nature of solar oscillation excitation and suggest an efficient algorithm to generate the artificial solar oscillation data. We also briefly discuss how the results of this work can be applied in the future studies.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.51.3-51.3
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• 2019

Solar magnetograms are important for studying solar activity and predicting space weather disturbances1. Farside magnetograms can be constructed from local helioseismology without any farside data2-4, but their quality is lower than that of typical frontside magnetograms. Here we generate farside solar magnetograms from STEREO/Extreme UltraViolet Imager (EUVI) $304-{\AA}$ images using a deep learning model based on conditional generative adversarial networks (cGANs). We train the model using pairs of Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) $304-{\AA}$ images and SDO/Helioseismic and Magnetic Imager (HMI) magnetograms taken from 2011 to 2017 except for September and October each year. We evaluate the model by comparing pairs of SDO/HMI magnetograms and cGAN-generated magnetograms in September and October. Our method successfully generates frontside solar magnetograms from SDO/AIA $304-{\AA}$ images and these are similar to those of the SDO/HMI, with Hale-patterned active regions being well replicated. Thus we can monitor the temporal evolution of magnetic fields from the farside to the frontside of the Sun using SDO/HMI and farside magnetograms generated by our model when farside extreme-ultraviolet data are available. This study presents an application of image-to-image translation based on cGANs to scientific data.