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The KMTNet 2016 Data Release

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arxiv 1804.03352 v1 pith:LNZ75HET submitted 2018-04-10 astro-ph.EP

H.-W. Kim , K.-H. Hwang , D.-J. Kim , M. D. Albrow , S.-M. Cha , S.-J. Chung , A. Gould , C. Han
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Y. K. Jung S.-L. Kim C.-U. Lee D.-J. Lee Y. Lee B.-G. Park R. W. Pogge Y.-H. Ryu I.-G. Shin Y.~Shvartzvald J. C. Yee W. Zang W. Zhu (KMTNet Collaboration)
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keywords microlensingcurvesdataeventsgammakmtnetlightrelease
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We present Korea Microlensing Telescope Network (KMTNet) light curves for microlensing-event candidates for the 2016 season, which covers an area of 97 sq.deg observed at cadences ranging from Gamma=0.2/hr to Gamma=8/hr from three southern sites in Chile, South Africa, and Australia. These 2163 light curves are comprised of 1856 "clear microlensing" and 307 "possible microlensing" events (including 265 previously released from the K2 C9 field). The data policy is very similar to the one governing the 2015 release. The changes relative to 2015 in the algorithms to find and vet microlensing events are comprehensively described.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. KMT-2025-BLG-2093: Free-Floating Planet Candidate Near the Shore of the Einstein Desert

    astro-ph.EP 2026-06 unverdicted novelty 5.0 of 10

    KMT-2025-BLG-2093 is identified as the second isolated microlens in the Einstein Desert, a free-floating planet candidate with θ_E = 13.1 ± 2.8 μas.

  2. KMT-2016-BLG-1836Lb: A Super-Jovian Planet From A High-Cadence Microlensing Field

    astro-ph.EP 2019-08 conditional novelty 5.0 of 10

    KMT-2016-BLG-1836Lb is a newly discovered microlensing planet with host-star mass ratio q about 0.004, with Bayesian estimates of about 2.2 Jupiter masses at 3.5 AU from an M or K dwarf.

  3. An Alternate Method for Minimizing $\chi^2$

    astro-ph.IM 2025-02 conditional novelty 3.0 of 10

    SFit implements the Gauss-Newton approximation for chi-square minimization and, in KMTNet point-lens fits, reports fewer false success and failure flags than BFGS while using fewer evaluations than Nelder-Mead.

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