Generalized model of laser-induced peak asymmetry in Raman lines
Bibliographic record
Abstract
The rate and precision at which samples can be scanned by Raman spectroscopy strongly depend on laser and material parameters. In this article, we describe the trade-off between parameters that increased laser intensities to improve resolution and reduce integration times, and its effect on thermally induced shift and asymmetric broadening of the line profile, especially in the case of resonant Raman. We present an analytical approximation to describe this phenomenon for all volumetrically absorbing materials and a wide range of laser parameters. This allows the determination of an optimal scan rate for the sample material and the required optical resolution, or vice versa, the determination and accurate correction for thermally induced shifts and asymmetries. This study provides an analytical quantification of this often-neglected line asymmetry and allows us to correct for its impact on the signal with few material properties and laser parameters. It may, in particular, allow us to discriminate this effect against other sources of peak asymmetry due to intrinsic properties. We obtain this analytical expression by condensing a parametrized finite element method model into a heuristic probability density function of temperature that describes the full parameter space. This function can be applied to any thermally undistorted line shape by convolution to determine a corrected line profile. This profile then provides a parameter-dependent optimized fitting function for an optimal determination of Raman signal parameters.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".