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OGLE-2017-BLG-1038: A Possible Brown-dwarf Binary Revealed by Spitzer Microlensing Parallax

Malpas et al. (2022)

Andrew P. Gould #4 Co-author Cha, Sang-Mok #15 Co-author Chung, Sun-Ju #16 Co-author Kyu-Ha Hwang #18 Co-author Jung Youn Kil #19 Co-author Dong-jin Kim #20 Co-author Kim Hyoun-Woo #21 Co-author Kim Seung-Lee #22 Co-author Lee, Chung-Uk #23 Co-author Dong-Joo Lee #24 Co-author Yongseok LEE #25 Co-author PARK, BYEONG GON #26 Co-author Yoon-Hyun Ryu #28 Co-author
THE ASTRONOMICAL JOURNAL IF 6.281 A 등급 International collaboration
KMTNet(외계행성 탐색시스템)

Abstract

We report the analysis of microlensing event OGLE-2017-BLG-1038, observed by the Optical Gravitational Lensing Experiment, Korean Microlensing Telescope Network, and Spitzer telescopes. The event is caused by a giant source star in the Galactic Bulge passing over a large resonant binary-lens caustic. The availability of space-based data allows the full set of physical parameters to be calculated. However, there exists an eightfold degeneracy in the parallax measurement. The four best solutions correspond to very-low-mass binaries near $( M_1 = 170^{+40}_{-50} M_{\rm J}$ {\rm and} $M_2 = 110^{+20}_{-30} M_{\rm J} )$, or well below $( M_1 = 22.5^{+0.7}_{-0.4} M_{\rm J} {\rm and} M_2 = 13.3^{+0.4}_{-0.3} M_{\rm J} )$ the boundary between stars and brown dwarfs. A conventional analysis, with scaled uncertainties for Spitzer data, implies a very-low-mass brown-dwarf binary lens at a distance of 2 kpc. Compensating for systematic Spitzer errors using a Gaussian process model suggests that a higher mass M-dwarf binary at 6 kpc is equally likely. A Bayesian comparison based on a galactic model favors the larger-mass solutions. We demonstrate how this degeneracy can be resolved within the next 10 years through infrared adaptive-optics imaging with a 40 m class telescope.

AI Classification

Maturity Tier

Tier 1 — Classical ML

AI Role

parameter-estimation

supporting

Techniques

gaussian-process

Tasks

regression/parameter-estimation

Subfield

other

multi

Flags

Develops AI method
· Classification confidence: 90%