A test of Ca II H & K photometry for isolating massive globular clusters below the metallicity floor
Bibliographic record
Abstract
Context. The serendipitous discovery of the M31 globular cluster (GC) EXT8 has presented a significant challenge to current theories of GC formation. By finding other GCs similar to EXT8, it should become clear if and/or how EXT8 can fit into our current understanding of GC formation. Aims. We aim to test the potential of integrated-light narrowband Ca II H & K photometry as a proxy for the metallicity of GCs to be able to provide effective candidate selection for massive GCs below the GC metallicity floor ([Fe/H] ≤ −2.5). Methods. We investigate the behaviour of two colours involving the CaHK filter employed by the Pristine survey, CaHK- u and CaHK- g , as a function of metallicity through CFHT MegaCam imaging of EXT8 and a wide set of M31 GCs covering the metallicity range −2.9 ≤ [Fe/H] ≤ +0.4. Additionally, we investigate if the CaHK colours are strongly influenced by horizontal branch morphology through available morphology measurements. Results. In both of the CaHK colours, EXT8 and two other potential GCs below the metallicity floor could be selected from other metal-poor GCs ([Fe/H] ≤ −1.5), with ( C a H K − g ) o showing the greater metallicity sensitivity. The RMS values of the linear fits to the metal-poor GCs for both colours show an uncertainty of 0.3 dex on metallicity estimations. Comparisons with u − g and g − z /F450W-F850L colours reinforce the notion that CaHK photometry can be used for effective candidate selection, as they reduce false positive selection rates by at least a factor of 2. We find no strong influence of the horizontal branch morphology on the CaHK colours that would interfere with candidate selection, although the assessment is limited by the quantity and quality of available data.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".