Mean First Passage Time for a Small Rotating Trap inside a Reflective Disk
Bibliographic record
Abstract
We compute the mean first passage time (MFPT) for a Brownian particle inside a two-dimensional disk with reflective boundaries and a small interior trap that is rotating at a constant angular velocity. The inherent symmetry of the problem allows for a detailed analytic study of the situation. For a given angular velocity, we determine the optimal radius of rotation that minimizes the average MFPT over the disk. Several distinct regimes are observed, depending on the ratio between the angular velocity $\omega$ and the trap size $\varepsilon$, and several intricate transitions are analyzed using the tools of asymptotic analysis and Fourier series. For $\omega\sim\mathcal{O}(1)$, we compute a critical value $\omega_c>0$ such that the optimal trap location is at the origin whenever $\omega<\omega_c$ and is off the origin for $\omega>\omega_c$. In the regime $1\ll\omega\ll\mathcal{O}(\varepsilon^{-1})$ the optimal trap path approaches the boundary of the disk. However, as $\omega$ is further increased to $\mathcal{O}(\varepsilon^{-1})$, the optimal trap path “jumps” closer to the origin. Finally, for $\omega\gg\mathcal{O}(\varepsilon^{-1})$ the optimal trap path subdivides the disk into two regions of equal area. This simple geometry provides a good test case for future studies of MFPT with more complex trap motion.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".