THE SEGUE K GIANT SURVEY. III. QUANTIFYING GALACTIC HALO SUBSTRUCTURE
Bibliographic record
Abstract
ABSTRACT We statistically quantify the amount of substructure in the Milky Way stellar halo using a sample of 4568 halo K giant stars at Galactocentric distances ranging over 5–125 kpc. These stars have been selected photometrically and confirmed spectroscopically as K giants from the Sloan Digital Sky Survey’s Sloan Extension for Galactic Understanding and Exploration project. Using a position–velocity clustering estimator (the 4distance) and a model of a smooth stellar halo, we quantify the amount of substructure in the halo, divided by distance and metallicity. Overall, we find that the halo as a whole is highly structured. We also confirm earlier work using blue horizontal branch (BHB) stars which showed that there is an increasing amount of substructure with increasing Galactocentric radius, and additionally find that the amount of substructure in the halo increases with increasing metallicity. Comparing to resampled BHB stars, we find that K giants and BHBs have similar amounts of substructure over equivalent ranges of Galactocentric radius. Using a friends-of-friends algorithm to identify members of individual groups, we find that a large fraction (∼33%) of grouped stars are associated with Sgr, and identify stars belonging to other halo star streams: the Orphan Stream, the Cetus Polar Stream, and others, including previously unknown substructures. A large fraction of sample K giants (more than 50%) are not grouped into any substructure. We find also that the Sgr stream strongly dominates groups in the outer halo for all except the most metal-poor stars, and suggest that this is the source of the increase of substructure with Galactocentric radius and metallicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".