New beyond-Voigt line-shape profile recommended for the HITRAN database
Bibliographic record
Abstract
Parameters associated with the collisional perturbation of spectral lines are essential for modeling the absorption of electromagnetic radiation in gas media. The HITRAN molecular spectroscopic database provides these parameters, although originally they were associated only with the Voigt profile parameterization. However, in the HITRAN2016 and HITRAN2020 editions, Voigt, speed-dependent Voigt and Hartmann-Tran (HT) profiles have been incorporated, thanks to the new relational structure of the database. The HT profile was introduced in HITRAN in 2016 as a recommended profile for the most accurate spectral interpretations and modeling. It was parameterized with a four-temperature-range temperature dependence. Since then, however, some features of the HT profile have been revealed that are problematic from a practical perspective. These are: the singular behavior of the temperature dependencies of the velocity-changing parameters when the shift parameter crosses zero and the difficulty in evaluating the former for mixtures. In this article, we summarize efforts to eliminate the above-mentioned problems that led us to recommend using the quadratic speed-dependent hard-collision (qSDHC) profile with double-power-law (DPL) temperature dependencies. We refer to this profile as a modified Hartmann-Tran (mHT) profile. The computational cost of evaluating it is the same as for the HT profile. We give a detailed description of the mHT profile (also including line mixing) and discuss the representation of its parameters, together with their DPL temperature parametrization adopted in the HITRAN database. We discuss an efficient algorithm for evaluating this profile and provide corresponding computer codes in several programming languages: Fortran, Python, MATLAB, Wolfram Mathematica, and LabVIEW. We also discuss the associated update of the HITRAN Application Programming Interface (HAPI).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.044 |
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".