Constraints on Ω<sub><i>m</i></sub>and σ<sub>8</sub>from Weak Lensing in Red‐Sequence Cluster Survey Fields
Bibliographic record
Abstract
We have analysed 53 square degrees of imaging data from the Red-Sequence Cluster Survey (RCS), and measured the excess correlations in the shapes of galaxies on scales out to ~1.5 degrees. We separate the signal into an ``E''- (lensing) and ``B''-mode (systematics), which allows us to study residual systematics. On scales larger than 10 arcminutes, we find no ``B''-mode. On smaller scales we find a small, but significant ``B''-mode. This signal is also present when we select a sample of bright galaxies. These galaxies are rather insensitive to observational distortions, and we therefore conclude that the oberved ``B''-mode is likely to be caused by intrinsic alignments. We therefore limit the cosmic shear analysis to galaxies with 22<R_C<24. We derive joint constraints on Omega_m and sigma_8, by marginalizing over Gamma, Omega_Lambda and the source redshift distribution, using different priors. We obtain a conservative constraint of $\sigma_8=0.45^{+0.09}_{-0.12} \Omega_m^{-0.55}$ (95% confidence). A better constraint is derived when we use Gaussian priors redshift distribution. For this choice of priors, we find $\sigma_8=(0.46^{+0.05}_{-0.07})\Omega_m^{-0.52}$ (95% confidence). Using our setof Gaussian priors, we find that we can place a lower bound on Gamma: Gamma>0.1+0.16\Omega_m$ (95% confidence). Comparison of the RCS results with three other recent cosmic shear measurements shows excellent agreement. The current weak lensing results are also in good agreement with CMB measurements, when we allow the reionization optical depth tau and the spectral index n_s to vary. We present a simple demonstration of how the weak lensing results can be used as a prior in the parameter estimation from CMB measurements to derive constraints on the reionization optical depth tau. (abridged)
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".