Interplay of pulse bandwidth and spectral resolution in spectral-focusing CARS microscopy
Bibliographic record
Abstract
We present a theoretical analysis of the relationship among the spectral resolution, the degree of chirp-matching, and pulse bandwidth in coherent anti-Stokes Raman scattering microscopy implementations based on spectral focusing (SF-CARS). We find that, despite allowing better ultimate spectral resolution when chirp-matching is attained, the use of the broadest bandwidth pulses can significantly worsen the spectral resolution if the pulses are not chirp-matched. We demonstrate that the bandwidth of the detected anti-Stokes signal is minimized when the pump is twice as chirped as the Stokes, meaning that—contrary to our initial presumption—a narrow anti-Stokes bandwidth does not imply good spectral resolution. We present approximate expressions that relate the bandwidths of the pump, Stokes, and anti-Stokes pulses to the degree of chirp-matching and outline how these could be used to estimate the amount of glass needed to attain chirp-matching. These expressions also permit a useful estimate of the group delay dispersion from other optics in the pump or Stokes beam path. This work aims to clarify several technical aspects of SF-CARS microscopy and provides researchers with valuable information to consider when working with SF-CARS microscopy systems.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".