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Systematic KMTNet Planetary Anomaly Search. XI. Complete Sample of 2016 Subprime Field Planets

Shin et al. (2024)

Lee, Chung-Uk #7 Co-author Chung, Sun-Ju #11 Co-author Kyu-Ha Hwang #12 Co-author Jung Youn Kil #13 Co-author Yoon-Hyun Ryu #14 Co-author Cha, Sang-Mok #16 Co-author Dong-jin Kim #17 Co-author Kim Seung-Lee #19 Co-author Dong-Joo Lee #20 Co-author Yongseok LEE #21 Co-author PARK, BYEONG GON #22 Co-author
ASTRONOMICAL JOURNAL IF 5.1 A 등급
KMTNet(외계행성 탐색시스템)

Abstract

Following Shin et al. (2023b), which is a part of the "Systematic KMTNet Planetary Anomaly Search" series (i.e., a search for planets in the 2016 KMTNet prime fields), we conduct a systematic search of the 2016 KMTNet subprime fields using a semi-machine-based algorithm to identify hidden anomalous events missed by the conventional by-eye search. We find four new planets and seven planet candidates that were buried in the KMTNet archive. The new planets are OGLE-2016-BLG-1598Lb, OGLE-2016-BLG-1800Lb, MOA-2016-BLG-526Lb, and KMT-2016-BLG-2321Lb, which show typical properties of microlensing planets, i.e., giant planets orbit M-dwarf host stars beyond their snow lines. For the planet candidates, we find planet/binary or 2L1S/1L2S degeneracies, which are an obstacle to firmly claiming planet detections. By combining the results of Shin et al. (2023b) and this work, we find a total of nine hidden planets, which is about half the number of planets discovered by eye in 2016. With this work, we have met the goal of the systematic search series for 2016, which is to build a complete microlensing planet sample. We also show that our systematic searches significantly contribute to completing the planet sample, especially for planet/host mass ratios smaller than 10-3, which were incomplete in previous by-eye searches of the KMTNet archive.

AI Classification

Maturity Tier

Tier 1 — Classical ML

AI Role

detection / segmentation

supporting

Techniques

classical-ML

Tasks

anomaly-detection

Subfield

exoplanets

time-series

Flags

Develops AI method
· Classification confidence: 90%