The NASA Ames PAH IR Spectroscopic Database: The Laboratory Spectra
Bibliographic record
Abstract
Abstract The astronomical emission features, formerly known as the unidentified infrared bands, are now commonly ascribed to polycyclic aromatic hydrocarbons (PAHs). The laboratory experiments and computational modeling performed at NASA Ames Research Center generated a collection of PAH IR spectra that have been used to test and refine the PAH model. These data have been assembled into the NASA Ames PAH IR Spectroscopic Database (PAHdb). PAHdb’s library of computed spectra, currently at version 3.20, contains data on more than 4000 species and the library of laboratory-measured spectra, currently at version 3.00, contains data on 84 species. The spectra can be perused and are available for download at www.astrochemistry.org/pahdb/ . This paper introduces the library of laboratory-measured spectra. Although it has been part of PAHdb since its inception, the library of laboratory-measured spectra lacked a proper description in the literature. Here, the experimental methods used to obtain the data are described in detail, an overview of the contents of the experimental library is given, and specific tools developed to analyze and interpret astronomical spectra with the laboratory data are discussed. In addition, updates to the website, documentation and software tools since our last reporting are presented. Software tools to work with the spectroscopic libraries are being developed actively and are available at GitHub. Lastly, a comprehensive demonstration showing how the laboratory-measured data can be applied to explore absorption features in observations toward embedded sources is presented. This demonstration suggests that PAHs very likely contribute to interstellar absorption spectra associated with dense clouds and underscores the need for further IR spectroscopic studies of PAHs trapped in water ice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.034 |
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".