Analog and digital simulations of Maxwellian plasmas forastrophysics
Bibliographic record
Abstract
Many astrophysical and laboratory plasmas possess Maxwell–Boltzmann (MB) electron energy distributions (EEDs). Interpreting or predicting the properties of these plasmas requires accurate knowledge of atomic processes such as radiative lifetimes, electron impact excitation and de-excitation, electron impact ionization, radiative recombination, dielectronic recombination, and charge transfer, all for thousands of levels or more. Plasma models cannot include all of the needed levels and atomic data. Hence, approximations need to be made to make the models tractable. Here we report on an “analog” technique we have developed for simulating a Maxwellian EED using an electron beam ion trap and review some recent results using this method. A subset of the atomic data needed for modeling Maxwellian plasmas relates to calculating the ionization balance. Accurate fractional abundance calculations for the different ionization stages of the various elements in the plasma are needed to reliably interpret or predict the properties of the gas. However, much of the atomic data needed for these calculations have not been generated using modern theoretical methods and are often highly suspect. Here we will also review our recent updating of the recommended atomic data for “digital’ computer simulations of MB plasmas in collisional ionization equilibrium (CIE), describe the changes relative to previously recommended CIE calculations, and discuss what further recombination and ionization data are needed to improve this latest set of recommended CIE calculations. PACS Nos.: 34.70.+e,34.80.Dp, 34.80.Kw, 34,80,Lx, 52.50.–j, 52.20.Fs, 52.20.Hv, 52.25.Jm, 52,72.+v, 52.75.–d, 95.30.Dr, 95.30.Ky, 98.38.Bn, 98.58.Bz
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".