Time- and frequency-resolved coherent anti-Stokes Raman scattering spectroscopy with sub-25fs laser pulses
Bibliographic record
Abstract
In general, many different diagrams can contribute to the signal measured in broadband four-wave mixing experiments. Care must therefore be taken when designing an experiment to be sensitive to only the desired diagram by taking advantage of phase matching, pulse timing, sequence, and the wavelengths employed. We use sub-25 fs pulses to create and monitor vibrational wavepackets in gaseous iodine, bromine, and iodine bromide through time- and frequency-resolved femtosecond coherent anti-Stokes Raman scattering (CARS) spectroscopy. We experimentally illustrate this using iodine, where the broad bandwidths of our pulses, and Boltzmann population in the lower three vibrational levels conspire to make a single diagram dominant in one spectral region of the signal spectrum. In another spectral region, however, the signal is the sum of two almost equally contributing diagrams, making it difficult to directly extract information about the molecular dynamics. We derive simple analytical expressions for the time- and frequency-resolved CARS signal to study the interplay of different diagrams. Expressions are given for all five diagrams which can contribute to the CARS signal in our case.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".