Bayesian method in optical emission spectroscopy: temporal evolution of electron density from time-integrated Hα emission and validation with time-resolved measurements for pulsed nanosecond discharges in water
Bibliographic record
Abstract
Abstract The characterization of in-liquid discharges is known to be a challenging feat due to their stochastic nature and nanosecond time scale evolution. In this study, the time-resolved electron density ( n e ) of a spark discharge in water is analyzed by coupling optical emission spectroscopy (OES) measurements with a Bayesian model. It is first highlighted that a single Voigt profile cannot adequately describe the time-averaged H α line profile; this is due to the significant time evolution of the discharge properties. To overcome this limitation, a model describing the temporal evolution of the line emission intensity and shape is developed and used to fit the time-integrated spectrum. The unknown parameters in the model are determined using the Dynesty python package, according to the Bayesian nested sampling method. With such model, the simulated and measured spectrum of the H α transition agree very well. Over the range of experimental conditions investigated, it is found that the electron density rapidly reaches ∼ 2 × 10 25 m − 3 and then decreases exponentially with a decay time of ∼ 238 ns . These values are consistent with those determined using time-resolved measurements and analysis of the H α and O I line broadenings. Overall, this study shows that time-resolved plasma properties can be obtained from time-integrated OES data by applying a Bayesian-based modeling approach. Further studies are needed to expand the scope of the developed model and determine plasma properties over a broad range of conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".