Ongoing astrometric microlensing events of two nearby stars
Bibliographic record
Abstract
Context. Astrometric microlensing is an excellent tool to determine the mass of stellar objects. By measuring the astrometric shift of a background source star in combination with precise predictions of its unlensed position and of the lens position, gravitational lensing allows to one determine the mass of the lensing star with a precision of 1%, independently of any prior knowledge. Aims. Making use of the recently published Gaia Data Release 2 (Gaia DR2) we predict astrometric microlensing events by fore-ground stars of high proper motion passing by a background star in the coming years. Methods. We compile a list of approximately 148 000 high-proper-motion stars within Gaia DR2 with µtot > 150 mas yr−1. We then search for background stars close to their paths and calculate the dates and separations of the closest approaches. Using color and absolute magnitude, we determine approximate masses of the lenses. Finally, we calculate the expected astrometric shifts and magnifications of the predicted events. Results. We detect two ongoing microlensing events by the high-proper-motion stars Luyten 143-23 and Ross 322 and predict closest separations of (108.5 ± 1.4) mas in July 2018 and (125.3 ± 3.4) mas in August 2018, respectively. The respective expected astrometric shifts are (1.74 ± 0.12) mas and (0.76 ± 0.06) mas. Furthermore, Luyten 143-23 will pass by another star in March 2021 with a closest separation of (280.1 ± 1.1) mas, which results in an expected shift of (0.69 ± 0.05) mas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".