Constraining lens masses in moderately to highly magnified microlensing events from Gaia
Bibliographic record
Abstract
Context. Microlensing events provide a unique way to detect and measure the masses of isolated, non-luminous objects, particularly dark stellar remnants. Under certain conditions, it is possible to measure the mass of these objects using photometry alone, specifically when a microlensing light curve displays a finite source (FS) effect. This effect generally occurs in highly magnified light curves, i.e. when the source and the lens are very well aligned. Aims. In this study, we analyse Gaia Alerts and Gaia Data Release 3 datasets, identifying four moderate-to-high-magnification microlensing events without a discernible FS effect. The absence of this effect suggests a large Einstein radius, implying substantial lens masses. Methods. In each event, we constrained the FS effect, and therefore established lower limits for the angular Einstein radius and lens mass. Additionally, we used the DarkLensCode software to obtain the mass, distance, and brightness distribution for the lens based on the Galactic model. Results. Our analysis established lower mass limits of ∼0.7 M ⊙ for one lens and ∼0.3 − 0.5 M ⊙ for two others. A DarkLensCode analysis supports these findings, estimating lens masses in the range of ∼0.42 − 1.70 M ⊙ and dark lens probabilities exceeding 80%. These results strongly indicate that the lenses are stellar remnants, such as white dwarfs or neutron stars. Conclusions. While further investigations are required to confirm the nature of these lenses, we demonstrate a straightforward yet effective approach to identifying stellar remnant candidates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".