The WIRCam Deep Survey
Bibliographic record
Abstract
We present a new near-infrared imaging survey in the four CFHTLS deep fields: the WIRCam Deep Survey or “WIRDS”. WIRDS comprises extremely deep, high quality (FWHM ~ 0.6″) J, H, and Ks imaging covering a total effective area of 2.1 deg2 and reaching AB 50% completeness limits of ≈ 24.5. We combine our images with the CFHTLS to create a unique eight-band ugrizJHKS photometric catalogues in the four CFHTLS deep fields; these four separate fields allow us to make a robust estimate of the effect of cosmic variance for all our measurements. We use these catalogues in combination with ≈ 9800 spectroscopic redshifts to estimate precise photometric redshifts (σΔz/(1 + z) ≲ 0.03 at i < 25), galaxy types, star-formation rates and stellar masses for a unique sample of ≈ 1.8 million galaxies. Our JHKs number counts are consistent with previous studies. We apply the “BzK” selection to our gzK filter set and find that the star forming BzK selection successfully selects 76% of star-forming galaxies in the redshift range 1.4 < z < 2.5 in our photometric catalogue, based on our photometric redshift measurement. Similarly the passive BzK selection returns 52% of the passive 1.4 < z < 2.5 population identified in the photometric catalogue. We present the mass functions of the total galaxy population as a function of redshift up to z = 2 and present fits using double Schechter functions. A mass-dependent evolution of the mass function is seen with the numbers of galaxies with masses of M ≲ 1010.75 still evolving at z ≲ 1, but galaxies of higher mass reaching their present day numbers by z ~ 0.8−1. This is consistent with the present picture of downsizing in galaxy evolution. We compare our results with the predictions of the GALFORM semi-analytical galaxy formation model and find that the simulations provide a relatively successful fit to the observed mass functions at intermediate masses (i.e. 10 ≲ log (M/M⊙) ≲ 11). However, as is common with semi-analytical predictions of the mass function, the GALFORM results under-predict the mass function at low masses (i.e. log (M/M⊙) ≲ 10), whilst the fit as a whole degrades beyondredshifts of z ~ 1.2. All photometric catalogues and images are made publicly available from TERAPIX and CADC.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".