Galaxy cluster searches based on photometric redshifts in the four CFHTLS Wide fields
Bibliographic record
Abstract
Context. Cosmological parameters can be constrained by counting clusters of galaxies as a function of mass and redshift and by considering regions of the sky sampled as deeply and as homogeneously as possible. Aims. Several methods for detecting clusters in large imaging surveys have been developed, among which the one used here, which is based on detecting structures. This method was first applied to the Canada France Hawaii Telescope Legacy Survey (CFHTLS) Deep 1 field by Mazure et al. (2007, A&A, 467, 49), then to all the Deep and Wide CFHTLS fields available in the T0004 data release by Adami et al. (2010, A&A, 509, A81). The validity of the cluster detection rate was estimated by applying the same procedure to galaxies from the Millennium simulation. Here we use the same method to analyse the full CFHTLS Wide survey, based on the T0006 data release. Methods. Our method is based on the photometric redshifts computed with Le Phare for all the galaxies detected in the Wide fields, limited to magnitudes z′ ≤ 22.5. We constructed galaxy density maps in photometric redshift bins of 0.1 based on an adaptive kernel technique, detected structures with SExtractor at various detection levels, and built cluster catalogues by applying a minimal spanning tree algorithm. Results. In a total area of 154 deg2, we have detected 4061 candidate clusters at 3σ or above (6802 at 2σ and above), in the redshift range 0.1 ≤ z ≤ 1.15, with estimated mean masses between 1.3 × 1014 and 12.6 × 1014 M⊙. This catalogue of candidate clusters will be available at the CDS. We compare our detections with those made in various CFHTLS analyses with other methods. By stacking a subsample of clusters, we show that this subsample has typical cluster characteristics (colour − magnitude relation, galaxy luminosity function). We also confirm that the cluster-cluster correlation function is comparable to the one obtained for other cluster surveys and analyse large-scale filamentary galaxy distributions. Conclusions. We have increased the number of known optical high-redshift cluster candidates by a large factor, an important step towards obtaining reliable cluster counts to measure cosmological parameters. The clusters that we detect behave as expected if they are located at the intersection of filaments by which they are fed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".