EXPERIMENTAL INVESTIGATIONS OF ION CHARGE DISTRIBUTIONS, EFFECTIVE ELECTRON DENSITIES, AND ELECTRON-ION CLOUD OVERLAP IN ELECTRON BEAM ION TRAP PLASMA USING EXTREME-ULTRAVIOLET SPECTROSCOPY
Bibliographic record
Abstract
Spectra in the extreme ultraviolet range from 107 to 353 Å emitted from Fe ions in various ionization stages have been observed at the Heidelberg electron beam ion trap (EBIT) with a flat-field grating spectrometer. A series of transition lines and their intensities have been analyzed and compared with collisional-radiative simulations. The present collisional-radiative model reproduces well the relative line intensities and facilitates line identification of ions produced in the EBIT. The polarization effect on the line intensities resulting from nonthermal unidirectional electron impact was explored and found to be significant (up to 24%) for a few transition lines. Based upon the observed line intensities, relative charge state distributions (CSD) of ions were determined, which peaked at Fe 23+ tailing toward lower charge states. Another simulation on ion charge distributions including the ionization and electron capture processes generated CSDs which are in general agreement with the measurements. By observing intensity ratios of specific lines from levels collisionally populated directly from the ground state and those starting from the metastable levels of Fe xxi , Fe x and other ionic states, the effective electron densities were extracted and found to depend on the ionic charge. Furthermore, it was found that the overlap of the ion cloud with the electron beam estimated from the effective electron densities strongly depends on the charge state of the ion considered, i.e. under the same EBIT conditions, higher charge ions show less expansion in the radial direction.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".