Young-Lo Kim
Papers
2
Total Citations
62
H-Index
2
About
Young-Lo Kim is a leading figure in time-domain astronomy, specializing in the automated classification and analysis of supernovae and other optical transients. His work directly addresses the critical bottleneck created by modern all-sky surveys, which now generate thousands of new transient detections each night. Kim’s major contributions include the development of key software modules for the SEDMachine, notably *byecr* and *contsep*, which robustly remove cosmic ray contamination and non-target light from spectra. This foundational work, published in 2022 and cited 57 times, has significantly improved the quality of automated spectral reduction. Building on this, Kim introduced **CCSNscore**, a multi-input deep learning tool specifically designed to classify core-collapse supernovae from SEDMachine spectra. This innovative framework, detailed in a 2025 paper, demonstrates a powerful, scalable solution for real-time classification, moving beyond traditional template-matching methods. By creating practical, open-source tools that enhance the throughput and accuracy of transient classification pipelines, Young-Lo Kim is playing a pivotal role in enabling the next generation of high-cadence, large-volume astronomical discovery.
Research Focus
Key Achievements
Top Papers
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