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Record W2137948139 · doi:10.1093/mnras/stt243

Studying wave optics in the light curves of exoplanet microlensing

2013· article· en· W2137948139 on OpenAlexaff
Ahmad Mehrabi, S. Rahvar

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsPerimeter Institute
Fundersnot available
KeywordsGravitational microlensingPhysicsCaustic (mathematics)ExoplanetLight curveGravitational lensAstronomyOpticsDiffractionPlanetExit pupilAstrophysicsStarsGalaxyPupil

Abstract

fetched live from OpenAlex

We study the wave optics features of gravitational microlensing by a binary lens composed of a planet and a parent star. In this system, the source star near the caustic line produces a pair of images in which they can play the role of secondary sources for the observer. This optical system is similar to the Young double-slit experiment. The coherent wavefronts from a source on the lens plane can form a diffraction pattern on the observer plane. This diffraction pattern has two modes from the close- and wide-pair images. From the observational point of view, we study the possibility of detecting this effect through the Square Kilometre Array (SKA) project in the resonance and high-magnification channels of binary lensing. While the red giant sources do not seem to satisfy the spatial coherency condition, during the caustic crossing a small part of a source traversing the caustic line can produce coherent pair images. Observations of wave optics effects at longer wavelengths accompanied by optical observations of a microlensing event provide extra information on the parameter space of the planet. These observations can provide a new basis for the study of exoplanets.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.200
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations12
Published2013
Admission routes1
Has abstractyes

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