KASI AI Hub KASI AI Hub

Systematic KMTNet planetary anomaly search. IV. Complete sample of 2019 prime-field

Zang et al. (2022)

Lee, Chung-Uk #4 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 I.-G. Shin #15 Co-author Cha, Sang-Mok #18 Co-author Dong-jin Kim #19 Co-author Kim Hyoun-Woo #20 Co-author Kim Seung-Lee #21 Co-author Dong-Joo Lee #22 Co-author Yongseok LEE #23 Co-author PARK, BYEONG GON #24 Co-author
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY IF 5.287 A 등급
KMTNet(외계행성 탐색시스템)

Abstract

We report the complete statistical planetary sample from the prime fields (Γ ≥ 2 h-1) of the 2019 Korea Microlensing Telescope Network (KMTNet) microlensing survey. We develop the optimized KMTNet AnomalyFinder algorithm and apply it to the 2019 KMTNet prime fields. We find a total of 13 homogeneously selected planets and report the analysis of three planetary events, KMT-2019-BLG-(1042,1552,2974). The planet-host mass ratios, q, for the three planetary events are 6.34 × 10-4, 4.89 × 10-3, and 6.18 × 10-4, respectively. A Bayesian analysis indicates the three planets are all cold giant planets beyond the snow line of their host stars. The 13 planets are basically uniform in log-q over the range -5.0 < log-q < -1.5. This result suggests that the planets below qbreak = 1.7 × 10-4 proposed by the MOA-II survey may be more common than previously believed. This work is an early component of a large project to determine the KMTNet mass-ratio function, and the whole sample of 2016-2019 KMTNet events should contain about 120 planets.

AI Classification

Maturity Tier

Tier 3 — Physics-tailored

AI Role

detection / segmentation

supporting

Techniques

classical-ML

Tasks

detection/segmentationanomaly-detection

Subfield

exoplanets

time-series

Flags

Develops AI method
· Classification confidence: 90%