Small-scale stellar haloes: detecting low surface brightness features in the outskirts of Milky Way dwarf satellites
Bibliographic record
Abstract
ABSTRACT Dwarf galaxies are valuable laboratories for dynamical studies related to dark matter and galaxy evolution, yet it is currently unknown just how physically extended their stellar components are. Satellites orbiting the Galaxy’s potential may undergo tidal stripping by the host, or alternatively, may themselves have accreted smaller systems whose debris populates the dwarf’s own stellar halo. Evidence of these past interactions, if present, is best searched for in the outskirts of the satellite. However, foreground contamination dominates the signal at these large radial distances, making observation of stars in these regions difficult. In this work, we introduce an updated algorithm for application to Gaia data that identifies candidate member stars of dwarf galaxies, based on spatial, colour–magnitude and proper motion information, and which allows for an outer component to the stellar distribution. Our method shows excellent consistency with spectroscopically confirmed members from the literature despite having no requirement for radial velocity information. We apply the algorithm to all ∼60 Milky Way dwarf galaxy satellites, and we find nine dwarfs (Boötes 1, Boötes 3, Draco 2, Grus 2, Segue 1, Sculptor, Tucana 2, Tucana 3, and Ursa Minor) that exhibit evidence for a secondary, low-density outer profile. We identify many member stars which are located beyond 5 half-light radii (and in some cases, beyond 10). We argue these distant stars are likely tracers of dwarf stellar haloes or tidal streams, though ongoing spectroscopic follow-up will be required to determine the origin of these extended stellar populations.
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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 | 0.002 | 0.001 |
| 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".