Improved Terahertz Time-Domain Spectroscopy via the Extended Kalman Filter
Bibliographic record
Abstract
To implement terahertz time-domain spectroscopy (THz-TDS) systems that have short integration time and maintain performance, the extended Kalman filter (EKF) has been used. Performance of EKF with THz-TDS on vapor samples and solid samples is untested. Previous EKF methods were not adaptive for use on multiple THz-TDS systems. We provide an adaptive EKF method that can work on any THz setup, to improve maximum measurable absorption coefficient. Our adaptive EKF method responds to THz pulse properties. The adaptive EKF is exhaustively demonstrated for six different time-constant data sets spanning 1 ms to 300 ms for THz-TDS on vapor with electro-optic THz detection. The acquisition time saved is quantified and shown to significantly reduce acquisition time from 2.58 ms to 1 ms. This improvement of 2.58 times will have a significant effect to move spectral imaging from taking hours to taking minutes. Vapor absorption lines are maintained. We show the adaptive EKF maintain correct spectral information when compared to the high-resolution transmission molecular absorption (HITRAN) database. The adaptive EKF is also demonstrated for THz-TDS on vapor with photoconductive THz detection, thus demonstrating adaptability, and is applied for THz-TDS of a solid sample of polyethylene terephthalate glycol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".