Galaxy clusters in the SDSS Stripe 82 based on photometric redshifts
Bibliographic record
Abstract
Context. The discovery of new galaxy clusters is important for two reasons. First, clusters are interesting per se, since their detailed analysis allows us to understand how galaxies form and evolve in various environments and second, they play an important part in cosmology because their number as a function of redshift gives constraints on cosmological parameters. Aims. We have searched for galaxy clusters in the Stripe 82 region of the Sloan Digital Sky Survey, and analysed various properties of the cluster galaxies. Methods. Based on a recent photometric redshift (hereafter photo-z) galaxy catalogue, we built a cluster catalogue by applying the Adami & MAzure Cluster FInder (AMACFI). Extensive tests were made to fine-tune the AMACFI parameters and make the cluster detection as reliable as possible. The same method was applied to the Millennium simulation to estimate our detection efficiency and the approximate masses of the detected clusters. Considering all the cluster galaxies (i.e. within a 1 Mpc radius of the cluster to which they belong and with a photo-z differing by less than ± 0.05 from that of the cluster), we stacked clusters in various redshift bins to derive colour–magnitude diagrams and galaxy luminosity functions (GLFs). For each galaxy brighter than Mr< − 19.0, we computed the disk and spheroid components by applying SExtractor, and by stacking clusters we determined how the disk-to-spheroid flux ratio varies with cluster redshift and mass. Results. We detected 3663 clusters in the redshift range 0.15 ≤ z ≤ 0.70, with estimated mean masses between ∼1013 and a few 1014 M⊙. We cross-matched our catalogue of candidate clusters with various catalogues extracted from optical and/or X-ray data. The percentages of redetected clusters are at most 40% because in all cases we detect relatively massive clusters, while other authors detect less massive structures. By stacking the cluster galaxies in various redshift bins, we find a clear red sequence in the (g′ − r′) versus r′ colour−magnitude diagrams, and the GLFs are typical of clusters, though with a possible contamination from field galaxies. The morphological analysis of the cluster galaxies shows that the fraction of late-type to early-type galaxies shows an increase with redshift (particularly in 9σ clusters) and a decrease with detection level, i.e. cluster mass. Conclusions. From the properties of the cluster galaxies, the majority of the candidate clusters detected here seem to be real clusters with typical cluster properties.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".