Topics in weak gravitational lensing
Bibliographic record
Abstract
In this thesis, various topics pertaining to weak gravitational lensing and its application to cosmology and galaxy evolution are explored. \nThe first chapter is the introduction which contains all of the background information needed to understand the rest of the thesis. Topics covered include cosmology, structure formation, the formation and evolution of galaxies, weak gravitational lensing, galaxy shape measurement, and simulations for the measurement and correction of biases in weak lensing surveys. \nIn the second chapter of this thesis, we present an analysis of weak lensing signals around galaxy groups and clusters using the data from the Canada-France Imaging Survey (CFIS), part of the Ultraviolet Near-Infrared Optical Northern Survey (UNIONS). Lenses are selected from the Tinker group catalogue and the redMaPPer cluster catalogue, and binned by estimated halo mass and richness. The weak lensing shape distortions around groups and clusters are then fit with simple models out to the virial radius. For redMaPPer clusters, we evaluate the mass-richness relation and find good agreement with previous results using other weak lensing data sets. We make the first weak lensing measurement of the masses of galaxy groups selected from the Tinker (2020a) catalogue, finding better agreement if the cosmological parameters have a lower value of 𝑆8 ≡ (Ωm/0.3)0.5𝜎8 = 0.74 ± 0.03. Additionally, we bin the groups by the colour of the central galaxy and confirm evidence for a bimodality in halo masses between groups with red and blue centrals for stellar masses > 10^11𝑀⊙. \nIn the third chapter, we present a weak lensing analysis of satellite galaxies in galaxy group \nenvironments. We find a mean satellite mass from satellites selected from the Tinker (2020a) catalogue of log_10 = 12.5 ± 0.2. Satellite galaxies in these environments are also predicted to be tidally stripped. We place a 1𝜎 lower limit on the truncation radius of 19 h^{-1} kpc. We then attempt to measure the truncation radius as a function of their projected separation from the group centre, but find that binning the satellites reduces the strength of the signal too significantly to measure such an effect. \nIn the final chapter of the thesis, we explore the topic of bias calibration in weak lensing surveys. A set of simulations designed to mock CFIS/UNIONS weak lensing observations is described. These simulations have a known shear applied to them, which can then attempt to be recovered via the weak lensing pipeline utilized by the survey. Initial measurements of the multiplicative and additive biases, which can be used to calibrate the shape measurements, are made.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".