Depth Profiling of Optical Absorption in Thin Films via the Mirage Effect and a New Inverse Scattering Theory. Part I: Principles and Methodology
Bibliographic record
Abstract
Impulse mirage effect spectroscopy is developed in this work as a nondestructive method for depth profiling the optical properties of samples which are nearly thermally homogeneous with depth. Both a theory and an experimental methodology are presented. An inverse scattering theory of the experimental photothermal deflection signal is derived, based on a previous theory of the impulse mirage effect, which takes into account the effect of Fresnel diffraction on the probe beam. To reconstruct the depth profile of heat source density generated by light absorption in an unknown sample, we have applied our inverse theory to the experimental impulse response, using a regularized minimum square error reconstruction algorithm based on our previously published expectation minimum principle. Because this reconstruction problem is ill posed, it was necessary to identify and compensate for all experimental bias errors significantly affecting the fidelity of the depth profiles. A procedure for obtaining the overall best-fit model of the depth profile given the minimum prior experimental information is presented. These procedures have produced an agreement between the experimental and theoretically predicted mirage effect response to within typical root-mean-square error levels of 0.5% or less.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".