The Spatial Distribution of Globular Cluster Systems in Early-type Galaxies: Estimation Procedure and Catalog of Properties for Globular Cluster Systems Observed with Deep Imaging Surveys
Bibliographic record
Abstract
Abstract We present an analysis of the spatial distribution of globular cluster (GC) systems of 118 nearby early-type galaxies in the Next Generation Virgo Cluster Survey and Mass Assembly of early-Type GaLAxies with their fine Structures survey programs, which both used MegaCam on the Canada–France–Hawaii Telescope. We describe the procedure used to select GC candidates and fit the spatial distributions of GCs to a two-dimensional Sérsic function, which provides effective radii (half number radii) and Sérsic indices, and estimate background contamination by adding a constant term to the Sérsic function. In cases where a neighboring galaxy affects the estimation of the GC spatial distribution in the target galaxy, we fit two two-dimensional Sérsic functions, simultaneously. We also investigate the color distributions of GCs in our sample by using Gaussian mixture modeling. For GC systems with bimodal color distributions, we divide the GCs into blue and red subgroups and fit their respective spatial distributions with Sérsic functions. Finally, we measure the total number of GCs based on our fitted Sérsic function, and calculate the GC specific frequency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".