Simulating ultracold matter : horizons and slow light
Bibliographic record
Abstract
This thesis explores the links between different ways of modelling the physical world. Finite difference numerical simulations may be used to encode the behaviour of physical systems, allowing us to gain insight into their workings and even to predict their behaviour. Similarly, one can investigate the properties of gravitational black holes through the use of analogue black holes, physical systems which share at least some part of the physics of the astronomical objects. Concentrating on black hole analogues using Bose-Einstein condensates, I show how simulations of these systems may be greatly assisted through the use of a proper absorbing boundary condition, the Perfectly Matched Layer. Such a boundary condition allows the effcient truncation of the computational domain, both saving computational time and increasing accuracy. I then apply this technique to the simulation of the supersonic flow of a Bose-Einstein condensate through a Laval nozzle, a black hole analogue, showing that such a flow should be stable and observable in the laboratory. Moving to a related system, I investigate the optical analogue of the Iordanskii force - the friction resulting from interaction between excitations in a superfluid's normal component and a superfluid vortex - through the simulation of such a vortex in a Bose-Einstein condensate illuminated by slow light, which is light whose group velocity is on the order of metres per second. The interaction of the slow light with the vortex should produce a momentum transfer due to the optical Aharonov-Bohm effect, exerting a force on the vortex. The coupled system of equations describing the condensate-slow light system is simulated, giving some surprising results.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".