Long wavelength mid-infrared from mixing two colors from a fiber amplifier
Bibliographic record
Abstract
At Waterloo, we are developing a high power, short pulse, two-color, Yb:fiber amplifier system to generate the long wavelength (<15μm) side of the molecular fingerprint spectral region, by difference frequency mixing the two colors. This spectral region is important for trace gas detection of explosives. As an example, it has been shown that the strong spectroscopic signatures of a peroxide-based explosive triacetone triperoxide (TATP) occur between 15 and 20 μm. To date, we have achieved a tuning range from 16 to 20 μm with a maximum average power of 1.7 mW. On the short wavelength side, the two colors would need to be pulled further apart, which requires a higher power seed to beat the amplified spontaneous emission that appears at the gain peak of the amplifiers between the two seed colors. On the long wavelength side, we are limited to 20 μm by the transparency region of the nonlinear crystals. We would like to find new nonlinear materials that have transparency from 1 to 30μm. If we could generate wavelengths from 15 to 30 μm with sufficient power, we could extend the spectral region to also cover 8 to 15μm by frequency doubling the longer wavelengths. We are currently working on replacing bulk optics in the system with fiber based optical elements to select the wavelengths as well as stretch and recompress the pulses in order to make the system compact and stable.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".