Comparison of the effect of soft-core potentials and Coulombic potentials on bremsstrahlung during laser matter interaction
Bibliographic record
Abstract
An intense, short laser pulse incident on rare-gas clusters can produce nano-plasmas containing energetic electrons. As these electrons undergo scattering, from both phonons and ions, they emit bremsstrahlung radiation. Here, we compare a theory of bremsstrahlung emission appropriate for the interaction of intense lasers with matter using soft-core potentials and Coulombic potentials. A new scaling for the radiation cross-section and the radiated power via bremsstrahlung is derived for a soft-core potential (which depends on the potential depth) and compared with the Coulomb potential. Calculations using the new scaling are performed for electrons in vacuum ultraviolet, infrared and mid-infrared laser pulses. The radiation cross-section and the radiation power via bremsstrahlung are found to increase rapidly with increases in the potential depth of up to around 200 eV and then become mostly saturated for larger depths while remaining constant for the Coulomb potential. In both cases, the radiation cross-section and the radiation power of bremsstrahlung decrease with increases in the laser wavelength. The ratio of the scattering amplitude for the soft-core potential and that for the Coulombic potential decreases exponentially with an increase in momentum transfer. The bremsstrahlung emission by electrons in plasmas may provide a broadband light source for diagnostics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".