Galaxy structure searches by photometric redshifts in the CFHTLS
Bibliographic record
Abstract
Context. Counting clusters is one of the methods to constrain cosmological parameters, but has been limited up to now both by the redshift range and by the relatively small sizes of the homogeneously surveyed areas.Aims. In order to enlarge publicly available optical cluster catalogs, in particular at high redshift, we have performed a systematic search for clusters of galaxies in the Canada France Hawaii Telescope Legacy Survey (CFHTLS).Methods. We considered the deep 2, 3 and 4 CFHTLS Deep fields (each 1 1 deg2), as well as the wide 1, 3 and 4 CFHTLS Wide fields. We used the Le Phare photometric redshifts for the galaxies detected in these fields with magnitude limits of i'=25 and 23 for the Deep and Wide fields respectively. We then constructed galaxy density maps in photometric redshift bins of 0.1 based on an adaptive kernel technique and detected structures with SExtractor at various detection levels. In order to assess the validity of our cluster detection rates, we applied a similar procedure to galaxies in Millennium simulations. We measured the correlation function of our cluster candidates. We analyzed large scale properties and substructures, including filaments, by applying a minimal spanning tree algorithm both to our data and to the Millennium simulations.Results. We detected 1200 candidate clusters with various masses (minimal masses between 1.0 1013 and 5.5 1013 and mean masses between 1.3 1014 and 12.6 10) in the CFHTLS Deep and Wide fields, thus notably increasing the number of known high redshift cluster candidates. We found a correlation function for these objects comparable to that obtained for high redshift cluster surveys. We also show that the CFHTLS deep survey is able to trace the large scale structure of the universe up to . Our detections are fully consistent with those made in various CFHTLS analyses with other methods. We now need accurate mass determinations of these structures to constrain cosmological parameters.Conclusions. We have shown that a search for galaxy clusters based on density maps built from galaxy catalogs in photometric redshift bins is successful and gives results comparable to or better than those obtained with other methods. By applying this technique to the CFHTLS survey we have increased the number of known optical high redshift cluster candidates by a large factor, an important step towards using cluster counts to measure cosmological parameters.
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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 | 0.002 |
| 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 | 0.001 | 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".