OGLE-2008-BLG-510: first automated real-time detection of a weak microlensing anomaly - brown dwarf or stellar binary?★
Bibliographic record
Abstract
The microlensing event OGLE-2008-BLG-510 is characterized by an evident asymmetric shape of the peak, promptly detected by the Automated Robotic Terrestrial Exoplanet Microlensing Search (ARTEMiS) system in real time. The skewness of the light curve appears to be compatible both with binary-lens and binary-source models, including the possibility that the lens system consists of an M dwarf orbited by a brown dwarf. The detection of this microlensing anomaly and our analysis demonstrate that: (1) automated real-time detection of weak microlensing anomalies with immediate feedback is feasible, efficient and sensitive, (2) rather common weak features intrinsically come with ambiguities that are not easily resolved from photometric light curves, (3) a modelling approach that finds all features of parameter space rather than just the ‘favourite model’ is required and (4) the data quality is most crucial, where systematics can be confused with real features, in particular small higher order effects such as orbital motion signatures. It moreover becomes apparent that events with weak signatures are a silver mine for statistical studies, although not easy to exploit. Clues about the apparent paucity of both brown-dwarf companions and binary-source microlensing events might hide here.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".