Constraints on the Galactic Inner Halo Assembly History from the Age Gradient of Blue Horizontal-branch Stars
Bibliographic record
Abstract
Abstract We present an analysis of the relative age distribution of the Milky Way halo, based on samples of blue horizontal-branch (BHB) stars obtained from the Panoramic Survey Telescope and Rapid Response System and Galaxy Evolution Explorer photometry, as well a Sloan Digital Sky Survey spectroscopic sample. A machine-learning approach to the selection of BHB stars is developed, using support vector classification, with which we produce chronographic age maps of the Milky Way halo out to 40 kpc from the Galactic center. We identify a characteristic break in the relative age profiles of our BHB samples, corresponding to a Galactocentric radius of R GC ∼ 14 kpc. Within the break radius, we find an age gradient of −63.4 ± 8.2 Myr kpc−1, which is significantly steeper than obtained by previous studies that did not discern between the inner- and outer-halo regions. The gradient in the relative age profile and the break radius signatures persist after correcting for the influence of metallicity on our spectroscopic calibration sample. We conclude that neither are due to the previously recognized metallicity gradient in the halo, as one passes from the inner-halo to the outer-halo region. Our results are consistent with a dissipational formation of the inner-halo population, involving a few relatively massive progenitor satellites, such as those proposed to account for the assembly of Gaia-Enceladus, which then merged with the inner halo of the Milky Way.
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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 | 0.002 |
| 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".