Detecting dark compact objects in Gaia DR4:A data analysis pipeline for transient astrometric lensing searches
Bibliographic record
Abstract
Abstract The Gaia satellite is cataloging the astrometric properties of an unprecedented number of stars in the Milky Way with extraordinary precision. This provides a gateway for conducting extensive surveys of transient astrometric lensing events caused by dark compact objects. In this work, we establish a data analysis pipeline capable of searching for such events in the upcoming Gaia Data Release 4 (DR4). We use Gaia Early Data Release 3 (EDR3) and current dark matter and astrophysical black hole population models to create mock DR4 catalogs containing stellar trajectories perturbed by lensing. Our analysis of these mock catalogs suggests that Gaia DR4 will contain about 4 astrometric lensing events from astrophysical black holes at a 5 σ significance level. Furthermore, we project that our data analysis pipeline applied to Gaia DR4 will result in leading constraints on compact dark matter in the mass range 1–10 3 M ⊙ down to a dark matter fraction of about one percent.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".