Combining intense terahertz pulses generation with broadband and real-time parametric detection
Bibliographic record
Abstract
Non-linear optical phenomena, such as parametric detection and amplification, manifest themselves in materials such as lithium niobate (LN) under the influence of a powerful optical pump beam. These processes have facilitated the practical realization of femtosecond (fs) pulse sources in the visible (VIS) and near infrared (NIR) spectra. They are also central to quantum detection, promising extremely sensitive detection of low-energy photons, particularly in the terahertz (THz) frequency range. To explore this innovative detection approach, we used an intense and powerful THz source taking advantage of optical rectification in lithium niobate (LN) crystals with an inclined-pulse front-end pumping configuration. By taking advantage of the high brightness of this source, we can acquire NIR signals in real time by upconversion and broadband using a standard CCD camera. In this presentation, we will look at the technical intricacies of the source and detection methodologies, as well as our goal of achieving single THz photon detection capability in the near future, all in the context of using ytterbium lasers.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".