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Systematic KMTNet Planetary Anomaly Search. VII. Complete Sample of q < 10-4 Planets from the First 4 yr Survey

Zang et al. (2023)

Systematic KMTNet Planetary Anomaly Search. VII. Complete Sample of q < 10 -4 Planets from the First 4 yr Survey

Jung Youn Kil #2 Co-author Chung, Sun-Ju #10 Co-author Kyu-Ha Hwang #12 Co-author Yoon-Hyun Ryu #13 Co-author Cha, Sang-Mok #16 Co-author Dong-jin Kim #17 Co-author Kim Seung-Lee #19 Co-author Lee, Chung-Uk #20 Co-author Dong-Joo Lee #21 Co-author Yongseok LEE #22 Co-author PARK, BYEONG GON #23 Co-author
THE ASTRONOMICAL JOURNAL IF 5.491 A 등급 International collaboration
KMTNet(외계행성 탐색시스템)

Abstract

We present the analysis of seven microlensing planetary events with planet/host mass ratios q < 10-4: KMT-2017-BLG-1194, KMT-2017-BLG-0428, KMT-2019-BLG-1806, KMT-2017-BLG-1003, KMT-2019-BLG-1367, OGLE-2017-BLG-1806, and KMT-2016-BLG-1105. They were identified by applying the Korea Microlensing Telescope Network (KMTNet) AnomalyFinder algorithm to 2016-2019 KMTNet events. A Bayesian analysis indicates that all the lens systems consist of a cold super-Earth orbiting an M or K dwarf. Together with 17 previously published and three that will be published elsewhere, AnomalyFinder has found a total of 27 planets that have solutions with q < 10-4 from 2016-2019 KMTNet events, which lays the foundation for the first statistical analysis of the planetary mass-ratio function based on KMTNet data. By reviewing the 27 planets, we find that the missing planetary caustics problem in the KMTNet planetary sample has been solved by AnomalyFinder. We also find a desert of high-magnification planetary signals (A >~65), and a follow-up project for KMTNet high-magnification events could detect at least two more q < 10 -4 planets per year and form an independent statistical sample.

AI Classification

Maturity Tier

Tier 1 — Classical ML

AI Role

detection / segmentation

supporting

Techniques

classical-ML

Tasks

detection/segmentation

Subfield

exoplanets

time-series

Flags

Develops AI method
· Classification confidence: 90%