McGill University M. Sc. Thesis: Topological Superconductivity without\n Proximity Effect
Bibliographic record
Abstract
The search for a Majorana Fermion has been an area of intense interest in\ncondensed matter research of late. This elusive particle, predicted to exist in\n1937, has been sought after for both fundamental and practical reasons. On the\nfundamental level, no particle to date has been observed to be a Majorana\nfermion, meanwhile on the practical level a Majorana fermion, if found, would\nrepresent a non-abelian anyon and could thus be used to build a quantum\ncomputer. The search for a Majorana Fermion has recently shifted to topological\nsuperconductivity. Topological superconductors are categorized by the\nnontrivial wind- ing of their order parameter phase and for this reason are\nexpected to support Majorana Fermions in their vortex cores. Owing to this, the\nstudy of topological superconductors has intensified in recent years. Current\nproposals for a device that may behave as a topological superconductor are\nbased on semiconductor heterostructures, where the spin-orbit coupled bands of\na semiconductor are split by a band gap or Zeeman field and superconductivity\nis induced by proximity to a conventional superconductor. In this setup,\ntopological superconductivity is obtained in the semiconductor layer and the\nproposed heterostructures typically include two or three layers of different\nmaterials. In this thesis we propose a simplification to these types of\ndevices, suggesting a way in which the superconducting layer can be replaced.\nPart of our proposal includes a model Hamiltonian for these types of systems.\nThis thesis will also develop several different methods to analyze this model\nHamiltonian in various different parameter regimes with the ultimate goal of\nclassifying its topology.\n
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.175 | 0.048 |
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".