The Canada-France Deep Fields-Photometric Redshift Survey: An Investigation of Galaxy Evolution Using Photometric Redshifts
Bibliographic record
Abstract
Progress in the study of galaxy evolution has traditionally followed from improvements in spectroscopic measurement techniques and subsequent groundbreaking surveys. The advent of large format CCD detectors, coupled with the demonstrated success of the photometric redshift method, has given rise to a new, potentially very powerful alternative. It has, in fact, motivated the present detailed investigation of the potential of photometric redshift surveys to complement, or in some cases, supersede traditional spectroscopic surveys in galaxy evolution studies. This Thesis describes a new deep, wide-field, multi-colour imaging survey, 10 times deeper and 30 times larger than its spectroscopic predecessor, the Canada-France Redshift Survey (CFRS). Highly accurate photometric redshifts, calibrated using hundreds of spectroscopic CFRS galaxies, were measured for tens of thousands of objects, with typical dispersions of only sigma/(1 + z) lsim; 0.06 to IAB = 24 for z le; 1.3. For the 1- and 2-point statistics of the galaxy distribution studied in this Thesis, the measurement accuracy is limited not by the photometric redshift error, but rather by the effect of cosmic variance, whose contribution to the total error budget is dominant. Therefore, future studies will be well served by adopting the photometric redshift approach, the efficiency of which will enable them to survey the hundreds or thousands of square degrees required to obtain a fair sample of the Universe. We measure the evolution of galaxy correlations with redshift, a primary observable of the structure formation process, correcting for the dilutive effect of photometric redshift errors on the clustering signal. The high z sim; 3 correlation amplitude seen in this work provides compelling evidence for the biased galaxy formation paradigm. The measured galaxy correlations from 0 lsim; z lsim; 3 are in excellent agreement with the findings of the largest, state-of-the-art spectroscopic studies. A new Bayesian method to measure the galaxy redshift distribution is developed. The accuracy of the method, which incorporates the full redshift likelihood function of each galaxy in an iterative analysis, is demonstrated in extensive Monte Carlo simulations. IAB and RAB redshift distributions, along with the run of median redshifts, are measured in various magnitude ranges, with special attention given to quantifying both random and systematic errors.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".