The WIRCam Deep Survey
Bibliographic record
Abstract
We present an analysis of the clustering of galaxies from z ≈ 2 to the present day using the WIRCam Deep Survey (WIRDS). WIRDS combines deep optical data from the CFHTLS Deep fields with its own deep near-infrared data, providing a photometric data-set over an effective area of 2.4 deg2, from which accurate photometric redshifts and stellar masses can be estimated. We use the data to calculate the angular correlation function for galaxy samples split by star-formation activity, stellar mass and redshift. Using WIRDS with its large total area and multiple fields gives a low cosmic variance contribution to the error, which we estimate to be less than ~2.8%. Based on power-law fits, we estimate the real-space clustering for each sample, determining clustering lengths and power-law slopes. For galaxies selected by constant mass, we find that the clustering scale shows no evolution up to z ≈ 2. Splitting the galaxy sample by mass, we see a consistent trend for higher mass galaxies to have larger clustering scales at all redshifts considered. We use our results to test the galform semi-analytical model of galaxy formation and evolution. The observed trends are well matched by the model galaxies for both the redshift evolution and the mass dependence of the galaxy clustering. We split the galaxy population into passive and star-forming populations based on rest-frame dust-corrected NUV-r colours. We find that the passive galaxy populations show a significantly larger clustering scale at all redshifts than the star-forming population below masses of M⋆ ~ 1011 h-1 M⊙, showing that even at z ≈ 2 passive galaxies exist in denser environments than the bulk of the star-forming galaxy population. For star-forming galaxies with stellar masses of M⋆ ≳ 1011 h-1 M⊙, we find a clustering strength of ~8 h-1 Mpc across all redshifts, comparable to the measurements for the passive population. Additionally, for star-forming galaxies we see that clustering strength increases for higher stellar mass systems, however little sign of a mass dependence in passive galaxies is observed over the range in stellar mass that is probed. Comparing our results to the model galaxy population produced by galform, we find a qualitative good agreement between the model predictions and the observed clustering. Finally, we investigate the connection between galaxy stellar mass and dark matter halo mass, showing a clear correlation between the two in both the WIRDS data and the galform predictions.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".