Astrometric weak lensing with <i>Gaia</i> DR3 and future catalogues: searches for dark matter substructure
Bibliographic record
Abstract
ABSTRACT Small-scale dark matter structures lighter than a billion solar masses are an important probe of primordial density fluctuations and dark matter microphysics. Due to their lack of starlight emission, their only guaranteed signatures are gravitational in nature. We report on results of a search for astrometric weak lensing by compact dark matter subhaloes in the Milky Way with Gaia DR3 data. Using a matched-filter analysis to look for correlated imprints of time-domain lensing on the proper motions of background stars in the Magellanic Clouds, we exclude order-unity substructure fractions in haloes with masses Ml between 107 and $10^9 \, {\rm M}_\odot$ and sizes of one parsec or smaller. We forecast that a similar approach based on proper accelerations across the entire sky with data from Gaia DR4 may be sensitive to substructure fractions of fl ≳ 10−3 in the much lower mass range of $10 \, {\rm M}_\odot \lesssim M_l \lesssim 3 \times 10^3 \, {\rm M}_\odot$. We further propose an analogous technique for stacked star–star lensing events in the regime of large impact parameters. Our first implementation is not yet sufficiently sensitive but serves as a useful diagnostic and calibration tool; future data releases should enable average stellar mass measurements using this stacking method.1
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.002 | 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".