Monitoring dynamic matter using the mesoscopic phase statistics of random wave fields
Bibliographic record
Abstract
In strongly scattering materials, multiple scattering tends to randomize the phase of transmitted or reflected waves and, as a result, the phase has often been overlooked. In this talk, the use of phase information to monitor the dynamics of multiply scattering media will be described and illustrated through measurements of the temporal fluctuations of ultrasonic waves transmitted through a time-varying mesoscopic sample. The probability distribution of the wrapped phase difference as a function of evolution time, as well as its variance, is measured and compared with theoretical predictions based on circular Gaussian (C1) statistics. Excellent agreement is found. 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 way of probing the motion of the scatterers in the medium. To investigate dynamics on longer time scales, we also investigate the variance and correlations of the cumulative phase. This combination of wrapped and cumulative phase measurements allows both the short and long time dynamics to be probed with excellent sensitivity.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".