Suhyun Shin
Seoul National University
15 Papers
76 Citations
Suhyun Shin is an academic researcher from Seoul National University. The author has contributed to research in topics: Quasar & Redshift. The author has an hindex of 7, co-authored 10 publications.
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Papers
Medium-band observation of the neutrino emitting blazar, TXS 0506+056
Sungyong Hwang,Myungshin Im,Yoon Chan Taak,Insu Paek,Changsu Choi,Suhyun Shin,Sang-Yun Lee,Tae Geun Ji,Soojong Pak,Hye In Lee,Hye In Lee,Hojae Ahn,Jimin Han,Changgon Kim,Jennifer L. Marshall,Christopher M. Johns-Krull,Coyne A. Gibson,Luke M. Schmidt,Travis Prochaska +18 more
TL;DR: In this paper, medium-band observations of TXS 0506+056 were used to examine if there were any significant changes in its spectral shapes over the course of one month and give a better constraint on the peak frequency of synchrotron radiation with quasi-simultaneous datasets.
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The Infrared Medium-deep Survey. IX. Discovery of Two New z ∼ 6 Quasars and Space Density Down to M 1450 ∼ −23.5 mag
Yongjung Kim,Myungshin Im,Y.-B. Jeon,Minjin Kim,Linhua Jiang,Suhyun Shin,Changsu Choi,Minhee Hyun,Hyunsung David Jun,Dohyeong Kim,Duho Kim,Jae-Woo Kim,Ji Hoon Kim,Bumhyun Lee,Seong-Kook Lee,Juan Molina,Soojong Pak,S. K. Park,Yoon Chan Taak,Yongmin Yoon +19 more
TL;DR: In this paper , the corrected Akaike information criterion (AICc) was used with the high-redshift quasar and late-type star models to prioritize the candidates efficiently.
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Estimators of Bolometric Luminosity and Black Hole Mass with Mid-infrared Continuum Luminosities for Dust-obscured Quasars: Prevalence of Dust-obscured SDSS Quasars
TL;DR: Researchers develop mid-infrared continuum luminosity-based estimators for bolometric luminosity and black hole mass in dust-obscured quasars, achieving ∼0.2 dex accuracy, and find that dust extinction significantly affects parameter derivations in a non-negligible fraction of Sloan Digital Sky Survey quasars.
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The Quasar Luminosity Function at z ∼ 5 via Deep Learning and Bayesian Information Criterion
TL;DR: In this paper , an artificial neural network was trained to distinguish z ∼ 5 quasars from non-quasar sources based on their colors and adopted the Bayesian information criterion that can further remove high-redshift galaxies from the quasar sample.
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Intensive Monitoring Survey of Nearby Galaxies (IMSNG)
Myungshin Im,Changsu Choi,Sungyong Hwang,Gu Lim,Joonho Kim,S. B. Kim,Gregory S. H. Paek,Sang-Yun Lee,Sung-Chul Yoon,Hyunjin Jung,Hyun-Il Sung,Yeong-beom Jeon,Shuhrat A. Ehgamberdiev,Otabek Burhonov,Davron Milzaqulov,Omon Parmonov,Sang Gak Lee,Wonseok Kang,Taewoo Kim,Sun-gill Kwon,Soojong Pak,Tae-Geun Ji,Hye-In Lee,Woojin Park,Hojae Ahn,Seoyeon Byeon,Jimin Han,Coyne A. Gibson,J. Craig Wheeler,John Kuehne,Christopher M. Johns-Krull,Jennifer L. Marshall,Minhee Hyun,Seong-Kook Lee,Yongjung Kim,Yongmin Yoon,Insu Paek,Suhyun Shin,Yoon Chan Taak,Juhyung Kang,Seoyeon Choi,Mankeun Jeong,Moo-Keon Jung,Hwara Kim,Jisu Kim,Dayae Lee,Bomi Park,Keunwoo Park,Seong A. O +48 more
TL;DR: The Intensive Monitoring Survey of Nearby Galaxies (IMSNG) is a high cadence observation program monitoring nearby galaxies with high probabilities of hosting supernovae (SNe) as mentioned in this paper.
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