Evidence of a population of dark subhaloes from <i>Gaia</i> and Pan-STARRS observations of the GD-1 stream
Bibliographic record
Abstract
ABSTRACT New data from the Gaia satellite, when combined with accurate photometry from the Pan-STARRS survey, allow us to accurately estimate the properties of the GD-1 stream. Here, we analyse the stellar density variations in the GD-1 stream and show that they cannot be due to known baryonic structures such as giant molecular clouds, globular clusters, or the Milky Way’s bar or spiral arms. A joint analysis of the GD-1 and Pal 5 streams instead requires a population of dark substructures with masses ≈107–$10^9 \ \rm {M}_{\odot }$. We infer a total abundance of dark subhaloes normalized to standard cold dark matter $n_{\rm sub}/n_{\rm sub, CDM} = 0.4 ^{+0.3}_{-0.2}$ (68 per cent), which corresponds to a mass fraction contained in the subhaloes $f_{\rm {sub}} = 0.14 ^{+0.11}_{-0.07} {{\ \rm per\ cent}}$, compatible with the predictions of hydrodynamical simulation of cold dark matter with baryons.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".