A New Method For Galaxy Cluster Detection. I. The Algorithm
Bibliographic record
Abstract
Numerous methods for finding clusters at moderate to high redshifts have been proposed in recent years, at wavelengths ranging from radio to X-rays. In this paper we describe a new method for detecting clusters in two-band optical/near-IR imaging data. The method relies upon the observation that all rich clusters, at all redshifts observed so far, appear to have a red sequence of early-type galaxies. The emerging picture is that all rich clusters contain a core population of passively evolving elliptical galaxies that are coeval and formed at high redshifts. The proposed search method exploits this strong empirical fact by using the red sequence as a direct indicator of overdensity. The fundamental advantage of this approach is that with appropriate filters, cluster elliptical galaxies at a given redshift are redder than all normal galaxies at lower redshifts. A simple color cut thus virtually eliminates all foreground contamination, even at significant redshifts. In this paper, one of a series of two, we describe the underlying assumptions and basic techniques of the method in detail and contrast the method with those used by other authors. We provide a brief demonstration of the effectiveness of the technique using a real photometric sample with redshift data, and from this we conclude that the method offers a powerful yet simple way of identify galaxy clusters. We find that the method can reliably detect structures to masses as small as groups with velocity dispersions of only ∼300 km s -1 , with redshifts for all detected structures estimated to an accuracy of ∼10%.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.011 |
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".