Theory and Applications of Wave Optics in the Gravitational and Plasma Lensing of Coherent Sources
Bibliographic record
Abstract
As the nascent fields of fast radio burst and gravitational wave astronomy grow in observational relevance, the lensing of these compact, coherent sources of radiation will play an important role in shaping our understanding of some of the fundamental questions of cosmology. Questions such as: what is the large-scale behaviour of space-time? what is the small-scale nature of dark matter? and what is the physical state of the invisible baryonic component of the universe? As such, the study of wave optics in astrophysical lensing contexts has recently received revived interest, as these sources are expected to exhibit wave effects when they are lensed in a wide variety of scenarios. The work presented in this thesis further develops the theory of coherent wave lensing by expanding upon the mathematical framework of Picard-Lefschetz theory for the evaluation of oscillatory path integrals. This novel technique reveals the fundamental importance of complex images in governing wave optics and the transition from diffractive to refractive optics. Not only does Picard-Lefschetz theory allow for exact numerical evaluations of the Kirchhoff-Fresnel integral which describes lensing, but it also provides a powerful conceptual tool for understanding wave effects across different optical regimes. In addition to developing new theoretical tools, this work discusses new applications of wave lensing from the detection of exo-galactic exoplanets, to extreme precision measurements of cosmic expansion, to probing the geometric structure of plasma in the interstellar and circumgalactic medium. The main purpose of this thesis as a whole is two-fold: 1. To meet the requirements for graduating with a Doctor of Philosophy at the University of Toronto, and 2. To provide a concise and pedagogically oriented introduction into the field of coherent gravitational and plasma lensing. While the former purpose could be minimally satisfied by linking together relevant publications with little more than a staple, the latter requires the construction of a coherent exposition so that a new graduate student, or precocious undergraduate, could read this work cover-to-cover and leave well-situated to pursue research in the further directions suggested by this work. Whether or not this imagined readership will ever materialize is another question beyond the scope of the present work.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".