Discovery of new globular clusters in the Sagittarius dwarf galaxy
Bibliographic record
Abstract
Context. Globular clusters (GCs) are witnesses of the past accretion events onto the Milky Way. In particular, the GCs of the Sagittarius (Sgr) dwarf galaxy are important probes of an on-going merger. Aims. Our main goal is to search for new GC members of this dwarf galaxy using the VISTA Variables in the Via Lactea Extended Survey (VVVX) near-infrared database combined with the Gaia Early Data Release 3 (EDR3) optical database. Methods. We investigated all VVVX-enabled discoveries of GC candidates in a region covering about 180 sq. deg. toward the bulge and the Sgr dwarf galaxy. We used multiband point-spread function photometry to obtain deep color-magnitude diagrams (CMDs) and luminosity functions (LFs) for all GC candidates, complemented by accurate Gaia-EDR3 proper motions (PMs) to select Sgr members and variability information to select RR Lyrae which are potential GC members. Results. After applying a strict PM cut to discard foreground bulge and disk stars, the CMDs and LFs for some of the GC candidates exhibit well defined red giant branches and red clump giant star peaks. We selected the best Sgr GCs, estimating their distances, reddenings, and associated RR Lyrae. Conclusions. We discover 12 new Sgr GC members, more than doubling the number of GCs known in this dwarf galaxy. In addition, there are 11 other GC candidates identified that are uncertain, awaiting better data for confirmation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".