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Record W2945559248 · doi:10.3847/1538-3881/ab237f

Spitzer Parallax of OGLE-2018-BLG-0596: A Low-mass-ratio Planet around an M Dwarf

2019· article· en· W2945559248 on OpenAlexaff

Bibliographic record

VenueThe Astronomical Journal · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
FundersNational Research Foundation of KoreaNational Science Foundation
KeywordsGravitational microlensingPlanetParallaxPlanetary systemBulgeExoplanetLens (geology)Planetary mass

Abstract

fetched live from OpenAlex

Abstract We report the discovery of a Spitzer microlensing planet OGLE-2018-BLG-0596Lb, with preferred planet-host mass ratio q ∼ 2 × 10 −4 . The planetary signal, which is characterized by a short (∼1 day) “bump” on the rising side of the lensing light curve, was densely covered by ground-based surveys. We find that the signal can be explained by a bright source that fully envelops the planetary caustic, i.e., a “Hollywood” geometry. Combined with the source proper motion measured from Gaia , the Spitzer satellite parallax measurement makes it possible to precisely constrain the lens physical parameters. The preferred solution, in which the planet perturbs the minor image due to lensing by the host, yields a Uranus-mass planet with a mass of M p = 13.9 ± 1.6 M ⊕ orbiting a mid M-dwarf with a mass of M h = 0.23 ± 0.03 M ⊙ . There is also a second possible solution that is substantially disfavored but cannot be ruled out, for which the planet perturbs the major image. The latter solution yields M p = 1.2 ± 0.2 M ⊕ and M h = 0.15 ± 0.02 M ⊙ . By combining the microlensing and Gaia data together with a Galactic model, we find in either case that the lens lies on the near side of the Galactic bulge at a distance D L ∼ 6 ± 1 kpc. Future adaptive optics observations may decisively resolve the major image/minor image degeneracy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.222
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2019
Admission routes1
Has abstractyes

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