Modélisation guidée par les données des fonctions d'étalement du point des télescopes terrestres et spatiaux
Bibliographic record
Abstract
Gravitational lensing is the distortion of the images of distant galaxies by intervening massive objects and constitutes a powerful probe of the Large Scale Structure of our Universe. Cosmologists use weak (gravitational) lensing to study the nature of dark matter and its spatial distribution. These studies require highly accurate measurements of galaxy shapes, but the telescope's instrumental response, or point spread function (PSF), deforms our observations. This deformation can be mistaken for weak lensing effects in the galaxy images, thus being one of the primary sources of systematic error when doing weak lensing science. Therefore, estimating a reliable and accurate PSF model is crucial for the success of any weak lensing mission. The PSF field can be interpreted as a convolutional kernel that affects each of our observations of interest that varies spatially, spectrally, and temporally. The PSF model needs to cope with these variations and is constrained by specific stars in the field of view. These stars, considered point sources, provide us with degraded samples of the PSF field. The observations go through different degradations depending on the properties of the telescope, including undersampling, an integration over the instrument's passband, and additive noise. We finally build the PSF model using these degraded observations and then use the model to infer the PSF at the position of galaxies. This procedure constitutes the ill-posed inverse problem of PSF modelling. The core of this thesis has been the development of new data-driven, also known as non-parametric, PSF models. We have developed a new PSF model for ground-based telescopes, coined MCCD, which can simultaneously model the entire focal plane. Consequently, MCCD has more available stars to constrain a more complex model. The method is based on a matrix factorisation scheme, sparsity, and an alternating optimisation procedure. We have included the PSF model in a high-performance shape measurement pipeline and used it to process ~3500 deg² of r-band observations from the Canada-France Imaging Survey. A shape catalogue has been produced and will be soon released. The main goal of this thesis has been to develop a data-driven PSF model that can address the challenges raised by one of the most ambitious weak lensing missions so far, the Euclid space mission. The main difficulties related to the Euclid mission are that the observations are undersampled and integrated into a single wide passband. Therefore, it is hard to recover and model the PSF chromatic variations from such observations. Our main contribution has been a new framework for data-driven PSF modelling based on a differentiable optical forward model allowing us to build a data-driven model for the wavefront. The new model coined WaveDiff is based on a matrix factorisation scheme and Zernike polynomials. The model relies on modern gradient-based methods and automatic differentiation for optimisation, which only uses noisy broad-band in-focus observations. Results show that WaveDiff can model the PSFs' chromatic variations and handle super-resolution with high accuracy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".