A MegaCam Survey of Outer Halo Satellites. I. Description of the Survey*<sup>†</sup>
Bibliographic record
Abstract
Abstract We describe a deep, systematic imaging study of satellites in the outer halo of the Milky Way. Our sample consists of 58 stellar overdensities—i.e., substructures classified as either globular clusters, classical dwarf galaxies, or ultra-faint dwarf galaxies—that are located at Galactocentric distances of R GC ≥ 25 kpc (outer halo) and out to ∼400 kpc. This includes 44 objects for which we have acquired deep, wide-field, g- and r-band imaging with the MegaCam mosaic cameras on the 3.6 m Canada–France–Hawaii Telescope and the 6.5 m Magellan-Clay telescope. These data are supplemented by archival imaging, or published gr photometry, for an additional 14 objects, most of which were discovered recently in the Dark Energy Survey (DES). We describe the scientific motivation for our survey, including sample selection, observing strategy, data reduction pipeline, calibration procedures, and the depth and precision of the photometry. The typical 5σ point-source limiting magnitudes for our MegaCam imaging—which collectively covers an area of ≈52 deg2—are g lim ≃ 25.6 and r lim ≃ 25.3 AB mag. These limits are comparable to those from the coadded DES images and are roughly a half-magnitude deeper than will be reached in a single visit with the Large Synoptic Survey Telescope. Our photometric catalog thus provides the deepest and most uniform photometric database of Milky Way satellites available for the foreseeable future. In other papers in this series, we have used these data to explore the blue straggler populations in these objects, their density distributions, star formation histories, scaling relations, and possible foreground structures.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".