The treasure behind the haystack: MUSE analysis of five recently discovered globular clusters
Bibliographic record
Abstract
Context. After the second data release of Gaia, the number of new globular cluster candidates has increased significantly. However, most of them need to be properly characterised, both spectroscopically and photometrically, by means of radial velocities, metallicities, and deeper photometric observations. Aims. Our goal is to provide an independent confirmation of the cluster nature of Gran 4, a recently discovered globular cluster, with follow-up spectroscopic observations. The derived radial velocity for individual stars, coupled with proper motions, allows us to isolate cluster members from field stars, while the analysis of their spectra allows us to derive metallicities. By including in the analysis the recently confirmed clusters Gran 1, 2, 3, and 5, we aim to completely characterise recently discovered globular clusters. Methods. Using Gaia DR3 and VVV catalogue data and MUSE at VLT observations, we selected cluster members based on their proper motions, radial velocities and their position in colour-magnitude diagrams. Furthermore, full spectral synthesis was performed on the cluster members, extracting surface parameters and metallicity from MUSE spectra. Finally, a completeness estimation was performed on the total globular cluster population of the Milky Way. Results. We confirm the nature of Gran 4, a newly discovered globular cluster behind the Galactic bulge, with a mean radial velocity of RV = −265.28 ± 3.92 km s−1 and a mean metallicity of [Fe/H]= − 1.72 ± 0.32 dex. Additionally, independent measurements of the metallicities were derived for Gran 1, 2, 3, and 5. We also revise the observational lower mass limit for a globular cluster to survive in the bulge and disc environment. We estimate that ∼12 − 26 globular clusters have still to be discovered on the other side of the Galaxy (i.e., behind the bulge, bar and disk), up to 20 kpc.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".