Investigation of electron beam effects on <i>L</i>-shell Mo plasma produced by a compact LC generator using pattern recognition
Bibliographic record
Abstract
In this paper, the effects of an electron beam on X-pinch-produced spectra of L-shell Mo plasma are investigated for the first time by principal component analysis (PCA); this analysis is compared with that of line ratio diagnostics. A spectral database for PCA extraction is arranged using a non-Local Thermodynamic Equilibrium (non-LTE) collisional radiative L-shell Mo model. PC vector spectra of L-shell Mo, including F, Ne, Na and Mg-like transitions are studied to investigate the polarization types of these transitions. PC1 vector spectra of F, Ne, Na and Mg-like transitions result in linear polarization of Stokes Q profiles. Besides, PC2 vector spectra show linear polarization of Stokes U profiles of 2p53s of Ne-like transitions which are known as responsive to a magnetic field [Träbert, Beiersdorfer, and Crespo López-Urrutia, Nucl. Instrum Methods Phys. Res., Sect. B 408, 107–109 (2017)]. A 3D representation of PCA coefficients demonstrates that addition of an electron beam to the non-LTE model generates quantized, collective clusters which are translations of each other that follow V-shaped cascade trajectories, except for the case f = 0.0. The extracted principal coefficients are used as a database for an Artificial Neural Network (ANN) to estimate the plasma electron temperature, density and beam fractions of the time-integrated, spatially resolved L-shell Mo X-pinch plasma spectrum. PCA-based ANNs provide an advantage in reducing the network topology, with a more efficient backpropagation supervised learning algorithm. The modeled plasma electron temperature is about Te ∼ 660 eV and density ne = 1 × 1020 cm−3, in the presence of the fraction of the beams with f ∼ 0.1 and centered energy of 5 keV.
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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".