• 천문학논총
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• 제38권2호
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• pp.59-73
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• 2023

Since the farside of the moon is a place to avoid artificial radio frequency interference (RFI) created by human civilization, it is a most suitable place for searching technosignature, which are signs of technological civilization in the universe, in the radio band. The RFI is a factor that makes the study of searching technosignature quite complicated because it is difficult to distinguish between technological signals produced by human and extraterrestrial civilizations. In this paper, we review why the farside of the moon is the best place to detect technosignature and also introduce radio observatories on the farside of the moon that have been proposed in radio astronomy. The SETI (Search for Extraterrestrial Intelligence) project on the farside of the moon is expected to be one of the main candidates for international collaboration research topics on lunar surface observatory.

• 천문학회보
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• 제46권1호
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• pp.41.3-42
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• 2021

Conventional global magnetic field maps, such as daily updated synoptic maps, have been constructed by merging together a series of observations from the Earth's viewing direction taken over a 27-day solar rotation period to represent the full surface of the Sun. It has limitations to predict real-time farside magnetic fields, especially for rapid changes in magnetic fields by flux emergence or disappearance. Here, we construct accurate synchronic magnetic field maps using frontside and AI-generated farside data. To generate the farside data, we train and evaluate our deep learning model with frontside SDO observations. We use an improved version of Pix2PixHD with a new objective function and a new configuration of the model input data. We compute correlation coefficients between real magnetograms and AI-generated ones for test data sets. Then we demonstrate that our model better generate magnetic field distributions than before. We compare AI-generated farside data with those predicted by the magnetic flux transport model. Finally, we assimilate our AI-generated farside magnetograms into the flux transport model and show several successive global magnetic field data from our new methodology.

• 천문학회보
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• 제44권1호
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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.

• 천문학회보
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• 제44권1호
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• pp.52.2-52.2
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• 2019

Global map of solar surface magnetic field, such as the synoptic map or daily synchronic frame, does not tell us real-time information about the far side of the Sun. A deep-learning technique based on Conditional Generative Adversarial Network (cGAN) is used to generate farside magnetograms from EUVI $304{\AA}$ of STEREO spacecrafts by training SDO spacecraft's data pairs of HMI and AIA $304{\AA}$. Farside(or backside) data of daily synchronic frames are replaced by the Ai-generated magnetograms. The new type of data is used to calculate the Potential Field Source Surface (PFSS) model. We compare the results of the global field with observations as well as those of the conventional method. We will discuss advantage and disadvantage of the new method and future works.

• 한국측량학회지
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• 제27권2호
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• pp.129-138
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• 2009

달 중력장은 달의 내부구조와 이분성 및 마그마 바다의 분화 진화과정을 이해하고, 더 나아가 달의 기원과 진화를 규명하기 위한 중요한 자료이다. 본 연구에서는 달 탐사의 역사와 달의 중력장 탐지 및 중력장모델의 발전과정을 고찰하였으며, 최근에 임무를 종료한 일본의 SELENE위성의 관측방식을 소개하고, SELENE위성의 궤도추적자료를 처리하여 개발된 달 중력장모델 SGM90d(SELENE Gravity Model)를 이용해 달 전체의 중력이상을 결정하였으며, 기존의 달 중력장 모델인 LP165P모델과 비교분석을 수행하였다. SGM90d를 분석해 본 결과, SELENE위성의 4중 도플러 관측방식은 달 뒷면의 중력장 직접 관측에 매우 효과적이었으며, 달 뒷면 중력장의 최초 결정은 달의 메스콘과 같은 중력이상의 상세한 분포를 확인하고 달의 이분성을 이해하는데 큰 도움이 되었다.

• 천문학회보
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• 제46권2호
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• pp.61.4-61.4
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• 2021

Searching for technosignatures is the fundamental tool for finding the evidence of the extraterrestrial life in the Universe along with searching for biosignatures. We summarize the current status of the radio SETI(Search for Extraterrestrial Intelligence) such as the Breakthrough Listen project and suggest a concept of the VLBI SETI with KVN(Korean VLBI Network). In addition, we introduce conceptual studies of the SETI on the surface of Moon's farside and in lunar orbit.