Bibliographic record
Abstract
Being galaxy clusters the most massive bound structures in the Universe, they represent a powerful tool to probe the large-scale structure predicted by the standard cosmological model, and to understand how environmental effects affect galaxy evolution. To conduct these studies and obtain reliable results, it is important to build complete and pure cluster catalogs. The use of these catalogs for cosmology requires accurate estimates of cluster mass. In this work, I describe the cluster detection algorithm that I developed during my PhD thesis : Red-GOLD, and the results that I obtained by applying i to current multi-wavelength surveys. My algorithm is based on the detection of galaxy overdensities and the characterisation of their red-sequence. The algorithm finds red galaxy overdensities with respect to the mean background. I select red galaxies using color predictions given by stellar population synthesis models and impose color limits as a function of redshift. Among those galaxies, I discern the early-type galaxies from their spectral type. I then identify cluster members using accurate photometric redshifts, and estimate the cluster candidate richness. I applied Red-GOLD to optical data coming from two different surveys, the Next Generation Virgo Cluster Survey (NGVS) and the Canada-France-Hawaii Telescope Lensing Survey (CFHTLS) and detected galaxy cluster candidates up to redshift z=1. I assessed the performances of my algorithm by applying it to simulated galaxy catalogs from the Millennium simulations. My cluster catalogue is complete at the 80% up to redshift z=1 and pure at 81%.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".