Efficient material parameters estimation with terahertz time-domain spectroscopy
Bibliographic record
Abstract
Existing parameter extraction techniques in the terahertz range utilize the magnitude and phase of the transmission function at different frequencies. The number of unknowns is larger than the number of available information creating a nonuniqueness problem. The estimation of the material thickness thus suffers from inaccuracies. We propose a novel optimization technique for the estimation of material refractive index in the terahertz frequency range. The algorithm is applied for materials with arbitrary frequency dependence. Dispersive dielectric models are embedded for accurate parameter extraction of a sample with unknown thickness. Instead of solving N expensive nonlinear optimization problems with different possible material thickness, our technique obtains the optimal material thickness by solving only one optimization problem. The solution of the utilized optimization problem is accelerated by estimating both the first order derivatives (gradient) and second order derivatives (Hessian) of the objective function and supplying them to the optimizer. Our approach has been successfully illustrated through a number of examples with different dispersive models. The examples include the characterization of carbon nanotubes. The technique has also been successfully applied to materials characterized by the Cole-Cole, Debye, and Lorentz models.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".