Stellar populations in the CFHTLS
Bibliographic record
Abstract
We present a stellar populations analysis of the first release of the CFHTLS (Canada-France-Hawai Telescope Legacy Survey) data. A detailed comparison between the Besançon model of the Galaxy and the first data release of the CFHTLS-Deep survey is performed by implementing the MEGACAM photometric system in this model using stellar atmosphere model libraries. The reliability of the theoretical libraries to reproduce the observed colours in the MEGACAM system is investigated. The locations of various stellar species like subdwarfs, white dwarfs, late-type and brown dwarfs, binary systems are identified. The contamination of the stellar sample by quasars and compact galaxies is quantified using spectroscopic data from the VIMOS-VLT Deep Survey (VVDS) as a function of i' magnitude and colour. A comparison between simulated counts using the standard IMF at low masses show that the number of very low mass dwarfs may have been underestimated in previous studies. These observations favour a power law IMF following with for or for for single stars. The resulting LF is in agreement with the local LF as measured from the 5 or 25 pc samples. It is in strong disagreement with the Zheng et al. (2001) LF measured from deep HST data. We show that this discrepancy can be understood as an indication of a different IMF at low masses at early epochs of the Galaxy compared to the local thin disc IMF.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".