Predicted microlensing events from analysis of <i>Gaia</i> Data Release 2
Bibliographic record
Abstract
Context. Astrometric microlensing can be used to make precise measurements of the masses of lens stars that are independent of their assumed internal physics. Such direct mass measurements, obtained purely by observing the gravitational effects of the stars on external objects, are crucial for validating theoretical stellar models. Specifically, astrometric microlensing provides a channel to direct mass measurements of single stars for which so few measurements exist. Microlensing events that also exhibit a detectable photometric signature provide even stronger lens mass constraints. Aims. I use the astrometric solutions and photometric measurements of ~1.7 billion stars provided by Gaia Data Release 2 (GDR2) to predict microlensing events during the nominal Gaia mission and beyond. This will enable astronomers to observe the entirety of each event, including the peak, with appropriate observing resources. The data collected will allow precise lens mass measurements for white dwarfs and low-mass main sequence stars (K and M dwarfs) helping to constrain stellar evolutionary models. Methods. I search for source-lens pairs in GDR2 that could potentially lead to microlensing events between 25th July 2014 and 25th July 2026. I estimate the lens masses using GDR2 photometry and parallaxes, and appropriate model stellar isochrones. Combined with the source and lens parallax measurements from GDR2, this allows the Einstein ring radius to be computed for each source-lens pair. By considering the source and lens paths on the sky, I calculate the microlensing signals that are to be expected. Results. I present a list of 76 predicted microlensing events. Nine and five astrometric events will be caused by the white dwarf stars LAWD 37 and Stein 2051 B, respectively. A further nine events will exhibit detectable photometric and astrometric signatures. Of the remaining events, ten will exhibit astrometric signals with peak amplitudes above 0.5 mas, while the rest are low-amplitude astrometric events with peak amplitudes between 0.131 and 0.5 mas. Five and two events will reach their peaks during 2018 and 2019, respectively. Five of the photometric events have the potential to evolve into high-magnification events, which may also probe for planetary companions to the lenses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 0.002 |
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".