KASI AI Hub KASI AI Hub

Systematic KMTNet Planetary Anomaly Search. II. Six New q<2×10^-4 Mass-ratio Planets

Hwang et al. (2022)

Kyu-Ha Hwang #1 Lead Chung, Sun-Ju #9 Co-author Jung Youn Kil #11 Co-author Yoon-Hyun Ryu #12 Co-author I.-G. Shin #13 Co-author Cha, Sang-Mok #16 Co-author Dong-jin Kim #17 Co-author Kim Hyoun-Woo #18 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 6.281 A 등급
KMTNet(외계행성 탐색시스템)

Abstract

We apply the automated AnomalyFinder algorithm of Paper I to 2018-2019 light curves from the ~13 deg^2 covered by the six KMTNet prime fields, with cadences Γ >= 2 hr^-1. We find a total of 11 planets with mass ratios q < 2 × 10^-4, including 6 newly discovered planets, 1 planet that was reported in Paper I, and recovery of 4 previously discovered planets. One of the new planets, OGLE-2018-BLG-0977Lb, is in a planetary caustic event, while the other five (OGLE-2018-BLG-0506Lb, OGLE-2018-BLG-0516Lb, OGLE-2019-BLG-1492Lb, KMT-2019-BLG-0253, and KMT-2019-BLG-0953) are revealed by a “dip” in the light curve as the source crosses the host-planet axis on the opposite side of the planet. These subtle signals were missed in previous by-eye searches. The planet-host separations (scaled to the Einstein radius), s, and planet-host mass ratios, q, are, respectively, (s, q × 10^5) = (0.88, 4.1), (0.96 ± 0.10, 8.3), (0.94 ± 0.07, 13), (0.97 ± 0.07, 18), (0.97 ± 0.04, 4.1), and (0.74, 18), where the “ ± ” indicates a discrete degeneracy. The 11 planets are spread out over the range -5 < log q < -3.7. Together with the two planets previously reported with q ∼ 10^-5 from the 2018-2019 nonprime KMT fields, this result suggests that planets toward the bottom of this mass-ratio range may be more common than previously believed.

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%