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ShinGeon Kim

Publications and source records attributed to ShinGeon Kim.

2 recordsLinked to original sources

7DT Insight: Variability in Young Stellar Objects

Photometric variability in young stellar objects (YSOs) provides critical insight into the mechanisms of mass accretion, disk evolution, and circumstellar extinction in early stellar evolution. We present an analysis of day-timescale optical variability in the Orion A central region using two-night 7-Dimensional Telescope (7DT) medium-band photometry obtained on March 23 and 24, 2024. The 7DT observations provide optical spectral sampling with 16 medium-band filters spanning 400--825 nm, enabling direct two-epoch comparisons. To remove satellite-trail contamination, we used an SSIM-based ResNet classifier (accuracy 0.97; F1 = 0.93) to exclude affected exposures. Subsequent photometry and two-epoch variability measurements yielded a working sample of 769 YSO candidates, among which we identified 110 variables ($\sim$14\%), including seven extreme cases with $|\Delta m_\lambda|>0.5$ mag. To describe the wavelength dependence of the variability, we compared five simple templates: extinction-like changes ($R_V =$ 3.1 and 5.5), a gray (wavelength-independent) change, and two spot-like toy models (hot and cold) implemented as two-temperature surface mixtures. The best-fit results are dominated by spot-like templates (37 cold-spot and 22 hot-spot objects), with 37 sources best matched by extinction-like templates and 14 by the gray template. The m650 excess fraction is higher in the hot-spot and gray templates than in the others. This could be compatible with more frequent line/veiling-related contributions in those groups, although the m650 excess is not a direct accretion diagnostic.

astro-ph.SR

Refined classification of YSOs and AGB stars by IR magnitudes, colors, and time-domain analysis with machine learning

We introduce a binary classification model, {\it the Double Filter Model}, utilizing various machine learning and deep learning methods to classify Young Stellar Objects (YSOs) and Asymptotic Giant Branch (AGB) stars. Since YSOs and AGB stars share similar infrared (IR) photometric characteristics due to comparable temperatures and the presence of circumstellar dust, distinguishing them is challenging and often leads to misclassification. While machine learning and deep learning techniques have helped reduce YSO-AGB misclassifications, achieving a reliable separation remains challenging. Given that YSOs and AGB stars exhibit distinct light curves resulting from different variability mechanisms, our Double Filter Model leverages light curve data to enhance classification accuracy. This approach uncovered YSOs and AGB stars that were misclassified in IR photometry and was validated against Taurus YSOs and spectroscopically confirmed AGB stars. We applied the model to the {\it Spitzer/IRAC Candidate YSO Catalog for the Inner Galactic Midplane} (SPICY) catalog for catalog refinement and identified potential AGB star contaminants.

astro-ph.SR