Broadband and tunable time-resolved THz system using argon-filled hollow-core photonic crystal fiber
Bibliographic record
Abstract
We demonstrate broadband, frequency-tunable, phase-locked terahertz (THz) generation and detection based on difference frequency mixing of temporally and spectrally structured near-infrared (NIR) pulses. The pulses are prepared in a gas-filled hollow-core photonic crystal fiber (HC-PCF), whose linear and nonlinear optical properties can be adjusted by tuning the gas pressure. This permits optimization of both the spectral broadening of the pulses due to self-phase modulation (SPM) and the generated THz spectrum. The properties of the prepared pulses, measured at several different argon gas pressures, agree well with the results of numerical modeling. Using these pulses, we perform difference frequency generation in a standard time-resolved THz scheme. As the argon pressure is gradually increased from 0 to 10 bar, the NIR pulses spectrally broaden from 3.5 to 8.7 THz, while the measured THz bandwidth increases correspondingly from 2.3 to 4.5 THz. At 10 bar, the THz spectrum extends to 6 THz, limited only by the spectral bandwidth of our time-resolved detection scheme. Interestingly, SPM in the HC-PCF produces asymmetric spectral broadening that may be used to enhance the generation of selected THz frequencies. This scheme, based on a HC-PCF pulse shaper, holds great promise for broadband time-domain spectroscopy in the THz, enabling the use of compact and stable ultrafast laser sources with relatively narrow linewidths (<4 THz).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".