Phase statistics of multiply scattered ultrasonic waves in dynamic mesoscopic systems
Bibliographic record
Abstract
In weakly scattering materials, detecting motion by measuring the change in phase of reflected ultrasonic waves forms the basis of the well-known technique of Doppler ultrasound. In strongly scattering media, these methods break down and the technique of diffusing acoustic wave spectroscopy (DAWS) was developed [Cowan et al., Phys. Rev. Lett. 85, 453 (2000)]. To explore the use of phase information to investigate the dynamics of multiply scattering media, the temporal fluctuations in the phase of ultrasonic waves transmitted through a time-varying mesoscopic sample have been measured. We have compared phase statistics and correlations to detailed theoretical predictions based on circular Gaussian (C1) statistics [Genack et al., Phys. Rev. Lett. 82, 412 (1999)]. So far, excellent agreement is found. The cumulative phase is found to undergo a Brownian type process, described by a phase diffusion coefficient. A fundamental relationship between the variance in the phase of the transmitted waves and the fluctuations in the phase of individual scattering paths is predicted theoretically and verified experimentally. This relationship not only gives deeper insight into the physics of the phase of multiply scattered waves, but also provides a new, mesoscopic way of probing the motion of the scatterers in the sample. a)Currently at Department of Physics, University of Toronto, Toronto, ON, Canada M5S 3E3
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".