Changwan Kim | Searcharxiv

arXiv2026

KMTNet Synoptic Survey of Southern Sky II: Data Reduction and Real-Time Transient Detection Pipeline

We present a comprehensive pipeline developed for the image processing of the KMTNet Synoptic Survey of the Southern Sky (KS4) Data Release 1. This pipeline encompasses several key processes, including data quality assurance, astrometry, photometric zero-point (ZP) calibration, bad pixel masking, image stacking, and difference image analysis (DIA). The astrometric solutions were validated by cross-matching with the Gaia EDR3 catalog, achieving sub-pixel astrometric accuracy (< 0.4 arcsec). To ensure spatial consistency, we divided each image into multiple subsections and confirmed that astrometric accuracy was maintained even at the edges. We performed a two-stage photometric calibration. Initial ZP solutions were computed for each individual image frame using the APASS DR9 and SkyMapper DR3 catalogs. Subsequently, we corrected residual spatial variations in the stacked images using Gaia XP photometry. This procedure yielded a 5-sigma depth of 22-23 AB mag across the BVRI bands, with root-mean-square errors of approximately 0.03 mag when referenced to Gaia stars in the magnitude range of 14-19 mag. The processed KS4 images span over 4,000 deg^2 of the southern sky, providing reference images suitable for DIA. This publicly available pipeline also supports real-time processing of newly acquired images, enabling prompt transient detection. We demonstrate its effectiveness through successful applications in gravitational-wave follow-up observations.

astro-ph.IM↗

arXiv2025

Investigating the Effects of Point Source Injection Strategies on KMTNet Real/Bogus Classification

Recently, machine learning-based real/bogus (RB) classifiers have demonstrated effectiveness in filtering out artifacts and identifying genuine transients in real-time astronomical surveys. However, the rarity of transient events and the extensive human labeling required for a large number of samples pose significant challenges in constructing training datasets for RB classification. Given these challenges, point source injection techniques, which inject simulated point sources into optical images, provide a promising solution. This paper presents the first detailed comparison of different point source injection strategies and their effects on classification performance within a simulation-to-reality framework. To this end, we first construct various training datasets based on Random Injection (RI), Near Galaxy Injection (NGI), and a combined approach by using the Korea Microlensing Telescope Network datasets. Subsequently, we train convolutional neural networks on simulated cutout samples and evaluate them on real, imbalanced datasets from gravitational wave follow-up observations for GW190814 and S230518h. Extensive experimental results show that RI excels at asteroid detection and bogus filtering but underperforms on transients occurring near galaxies (e.g., supernovae). In contrast, NGI is effective for detecting transients near galaxies but tends to misclassify variable stars as transients, resulting in a high false positive rate. The combined approach effectively handles these trade-offs, thereby balancing between detection rate and false positive rate. Our results emphasize the importance of point source injection strategy in developing robust RB classifiers for transient (or multi-messenger) follow-up campaigns.

astro-ph.IM↗