Investigation of electron beam effects on <i>L</i>-shell Mo plasma produced by a compact LC generator using pattern recognition
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it