Yong‐Jae Moon

6.4k total citations
243 papers, 4.6k citations indexed

About

Yong‐Jae Moon is a scholar working on Astronomy and Astrophysics, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, Yong‐Jae Moon has authored 243 papers receiving a total of 4.6k indexed citations (citations by other indexed papers that have themselves been cited), including 236 papers in Astronomy and Astrophysics, 58 papers in Molecular Biology and 49 papers in Artificial Intelligence. Recurrent topics in Yong‐Jae Moon's work include Solar and Space Plasma Dynamics (227 papers), Ionosphere and magnetosphere dynamics (151 papers) and Stellar, planetary, and galactic studies (67 papers). Yong‐Jae Moon is often cited by papers focused on Solar and Space Plasma Dynamics (227 papers), Ionosphere and magnetosphere dynamics (151 papers) and Stellar, planetary, and galactic studies (67 papers). Yong‐Jae Moon collaborates with scholars based in South Korea, United States and India. Yong‐Jae Moon's co-authors include Haimin Wang, Jongchul Chae, G. S. Choe, Y. D. Park, A. Shanmugaraju, M. Dryer, Eunsu Park, K.‐S. Cho, Kyung‐Suk Cho and Su‐Chan Bong and has published in prestigious journals such as Physical Review Letters, Journal of Geophysical Research Atmospheres and The Astrophysical Journal.

In The Last Decade

Yong‐Jae Moon

229 papers receiving 4.3k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Yong‐Jae Moon South Korea 34 4.4k 1.2k 676 262 230 243 4.6k
Astrid Veronig Austria 46 7.1k 1.6× 1.6k 1.3× 764 1.1× 222 0.8× 275 1.2× 269 7.3k
Manuela Temmer Austria 46 6.6k 1.5× 1.6k 1.3× 631 0.9× 227 0.9× 296 1.3× 206 6.7k
W. D. Pesnell United States 24 3.8k 0.9× 662 0.6× 652 1.0× 138 0.5× 224 1.0× 93 4.0k
A. Vourlidas United States 57 9.5k 2.2× 2.0k 1.7× 694 1.0× 209 0.8× 243 1.1× 277 9.7k
T. L. Duvall United States 35 5.0k 1.1× 1.4k 1.2× 1.0k 1.5× 275 1.0× 633 2.8× 126 5.3k
J. Schou United States 39 7.0k 1.6× 1.9k 1.6× 1.1k 1.7× 101 0.4× 551 2.4× 149 7.4k
J. T. Hoeksema United States 41 8.3k 1.9× 2.6k 2.2× 1.3k 1.9× 151 0.6× 432 1.9× 174 8.5k
Phillip C. Chamberlin United States 30 5.4k 1.2× 673 0.6× 699 1.0× 377 1.4× 164 0.7× 93 5.7k
W. B. Manchester United States 33 4.3k 1.0× 1.3k 1.1× 275 0.4× 228 0.9× 166 0.7× 128 4.5k

Countries citing papers authored by Yong‐Jae Moon

Since Specialization
Citations

This map shows the geographic impact of Yong‐Jae Moon's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yong‐Jae Moon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yong‐Jae Moon more than expected).

Fields of papers citing papers by Yong‐Jae Moon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yong‐Jae Moon. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yong‐Jae Moon. The network helps show where Yong‐Jae Moon may publish in the future.

Co-authorship network of co-authors of Yong‐Jae Moon

This figure shows the co-authorship network connecting the top 25 collaborators of Yong‐Jae Moon. A scholar is included among the top collaborators of Yong‐Jae Moon based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yong‐Jae Moon. Yong‐Jae Moon is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Park, Eunsu, et al.. (2025). Artificial-intelligence-based Reconstruction of Solar Farside Vector Magnetograms from Multispacecraft Extreme-ultraviolet Data. The Astrophysical Journal Supplement Series. 281(2). 63–63.
2.
Moon, Yong‐Jae, et al.. (2025). Six-hour Prediction of Interplanetary Magnetic Field B z Profiles for Strong Southward Cases by Deep Learning. The Astrophysical Journal. 984(1). 67–67.
3.
Moon, Yong‐Jae, et al.. (2025). Real-time Extrapolation of Nonlinear Force-free Fields from Photospheric Vector Magnetic Fields Using a Physics-informed Neural Operator. The Astrophysical Journal Supplement Series. 277(2). 54–54. 1 indexed citations
4.
Moon, Yong‐Jae, et al.. (2024). Near-real-time 3D Reconstruction of the Solar Coronal Parameters Based on the Magnetohydrodynamic Algorithm outside a Sphere Using Deep Learning. The Astrophysical Journal Supplement Series. 271(1). 14–14. 3 indexed citations
5.
Bučík, Radoslav, et al.. (2024). Fe/O Variations Relative to Source Longitude and Heliospheric Current Sheet in Large Solar Energetic Particle Events. The Astrophysical Journal. 977(1). 86–86.
6.
Shanmugaraju, A., et al.. (2023). Solar active region magnetic parameters and their relationship with the properties of halo coronal mass ejections. Journal of Atmospheric and Solar-Terrestrial Physics. 249. 106106–106106. 5 indexed citations
7.
Shanmugaraju, A., et al.. (2023). Analysis of Front Side Halo CMEs and Their Solar Source Active Region and Flare Ribbon Properties. Solar Physics. 298(2). 1 indexed citations
8.
Park, Eunsu, Harim Lee, Yong‐Jae Moon, et al.. (2023). Pixel-to-pixel Translation of Solar Extreme-ultraviolet Images for DEMs by Fully Connected Networks. The Astrophysical Journal Supplement Series. 264(2). 33–33. 2 indexed citations
9.
Moon, Yong‐Jae, et al.. (2023). Examining the Source Regions of Solar Energetic Particles Using an AI-generated Synchronic Potential Field Source Surface Model. The Astrophysical Journal. 953(2). 159–159. 1 indexed citations
10.
Moon, Yong‐Jae, et al.. (2022). Improved AI-generated Solar Farside Magnetograms by STEREO and SDO Data Sets and Their Release. The Astrophysical Journal Supplement Series. 262(2). 50–50. 20 indexed citations
11.
Moon, Yong‐Jae, et al.. (2021). Visual Explanation of a Deep Learning Solar Flare Forecast Model and Its Relationship to Physical Parameters. The Astrophysical Journal. 910(1). 8–8. 24 indexed citations
12.
Park, Eunsu, Yong‐Jae Moon, Daye Lim, & Harim Lee. (2020). De-noising SDO/HMI Solar Magnetograms by Image Translation Method Based on Deep Learning. The Astrophysical Journal Letters. 891(1). L4–L4. 12 indexed citations
13.
Kim, Tae Young, Eunsu Park, Harim Lee, et al.. (2019). Solar farside magnetograms from deep learning analysis of STEREO/EUVI data. Nature Astronomy. 3(5). 397–400. 75 indexed citations
14.
Park, Eunsu, Yong‐Jae Moon, Jin‐Yi Lee, et al.. (2019). Generation of Solar UV and EUV Images from SDO/HMI Magnetograms by Deep Learning. The Astrophysical Journal Letters. 884(1). L23–L23. 33 indexed citations
15.
Joshi, Navin Chandra, Alphonse C. Sterling, Ronald L. Moore, Tetsuya Magara, & Yong‐Jae Moon. (2017). Onset of a Large Ejective Solar Eruption from a Typical Coronal-jet-base Field Configuration. The Astrophysical Journal. 845(1). 26–26. 25 indexed citations
16.
Nakariakov, V. M., et al.. (2016). Effect of a radiation cooling and heating function on standing longitudinal \noscillations in coronal loops \n. Warwick Research Archive Portal (University of Warwick). 15 indexed citations
17.
Joshi, Bhuwan, et al.. (2015). Evolutionary aspects and north-south asymmetry of soft X-ray flare index during solar cycles 21, 22, and 23. Springer Link (Chiba Institute of Technology). 29 indexed citations
18.
Moon, Yong‐Jae, et al.. (2014). Forecast of solar proton flux profiles for well‐connected events. Journal of Geophysical Research Space Physics. 119(12). 9383–9394. 8 indexed citations
19.
Moon, Yong‐Jae, et al.. (2014). What flare and CME parameters control the occurrence of solar proton events?. Journal of Geophysical Research Space Physics. 119(12). 9456–9463. 11 indexed citations
20.
Moon, Yong‐Jae, et al.. (1996). ALTERNATIVE FLARE ACTIVITY INDICATOR : MAD. Journal of The Korean Astronomical Society. 29. 323–324.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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