Bayesian galaxy shape measurement for weak lensing surveys - II. Application to simulations
Bibliographic record
Abstract
In this paper, we extend the Bayesian model fitting shape measurement method presented in Miller et al., and use the method to estimate the shear from the Shear TEsting Programme simulations (STEP). The method uses a fast model fitting algorithm that uses realistic galaxy profiles and analytically marginalizes over the position and amplitude of the model by doing the model fitting in Fourier space. This is used to find the full posterior probability in ellipticity. The shear is then estimated in a Bayesian way from this posterior probability surface. The Bayesian estimation allows measurement bias arising from the presence of random noise to be removed. In this paper, we introduce an iterative algorithm that can be used to estimate the intrinsic ellipticity prior and show that this is accurate and stable. We present results using the STEP parametrization that relates the input shear γT to the estimated shear γM by introducing a bias m and an offset c: γM−γT=mγT+c. The average number density of galaxies used in the STEP1 analysis was 9 per square arcminute, for STEP2 the number density was 30 per square arcminute. By using the method to estimate the shear from the STEP1 simulations we find the method to have a shear bias of m= 0.006 ± 0.005 and a variation in shear offset with point spread function type of σc= 0.0002. Using the method to estimate the shear from the STEP2 simulations we find that the shear bias and offset are m= 0.002 ± 0.016 and c=−0.0007 ± 0.0006, respectively. In addition, we find that the bias and offset are stable to changes in the magnitude and size of the galaxies. Such biases should yield any cosmological constraints from future weak lensing surveys robust to systematic effects in shape measurement. Finally, we present an alternative to the STEP parametrization by using a quality factor that relates the intrinsic shear variance in a simulation to the variance in shear that is measured and show that the method presented has an average of Q ≳ 100 which is at least a factor of 10 times better than other shape measurement methods.
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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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".