Lensing by galaxies in CNOC2 fields
Bibliographic record
Abstract
We have observed two blank fields of approximately 30 × 23 arcmin2 using the William Herschel Telescope. The fields have been studied as part of the Canadian Network for Observational Cosmology Field Galaxy Redshift Survey (CNOC2), and spectroscopic redshifts are available for 1125 galaxies in the two fields. We measured the lensing signal caused by large-scale structure, and found that the result is consistent with current, more accurate measurements. We study the galaxy—galaxy lensing signal of three overlapping samples of lenses (one with and two without redshift information), and detect a significant signal in all cases. The estimates for the velocity dispersion of an galaxy agree well for the various samples. The best-fitting singular isothermal sphere model to the ensemble-averaged tangential distortion around the galaxies with redshifts yields a velocity dispersion of σ*= 130+15−17 km s−1, or a circular velocity of V*c= 184+22−25 km s−1 for an L*B galaxy, in good agreement with other studies. We use a maximum-likelihood analysis, where a parametrized mass model is compared with the data, to study the extent of galaxy dark matter haloes. Making use of all available data, we find σ*= 111 ± 12 km s−1 (68.3 per cent confidence, marginalized over the truncation parameter s) for a truncated isothermal sphere model in which all galaxies have the same mass-to-light ratio. The value of the truncation parameter s is not constrained that well, and we find s*= 260+124−73h−1 kpc (68.3 per cent confidence, marginalized over σ*), with a 99.7 per cent confidence lower limit of 80 h−1 kpc. Interestingly, our results provide a 95 per cent confidence upper limit of 556 h−1 kpc. The galaxy—galaxy lensing analysis allows us to estimate the average mass-to-light ratio of the field, which can be used to estimate Ωm. The current result, however, depends strongly on the assumed scaling relation for s.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".