Dispersión inelástica de la luz por excitaciones electrónicas en átomos artificiales
Bibliographic record
Abstract
In this article we present a theoretical investigation of the inelastic light scattering by electronic excitations in a quantum dot charged with 42 electrons. The energies and wave-functions of the multielectronic states involved in the Raman process, are obtained in the framework of Random Phase Approximations (RPA). Charge-Density (CDE) and Spin-Density (SDE) excitations are identified by evaluating the multipolar energy-weighted sum rules (EWSR). We compute Raman spectra for different values of the incident laser energy in both, polarized and depolarized geometry. Calculated Raman spectra with anexcitation energy below the bandgap reveals several advantages for identifying and following individual peaks. The breakdown of Raman polarization selection rules under the influence of an external magnetic field is studied by calculating the polarization ratios. The breakdown of these selection rules under resonant excitation with the semiconductor bandgap, which we termed Raman intensity jump-rule, is found and proposed as a useful tool for identifying the character (charge or spin) of electronic excitations. We found that Raman spectra in the extreme resonance region are dominated by strong peaks associated to single-particle excitations (SPEs). The interference effects between the intermediate states in the Raman transition amplitude are evaluated. The main features of Raman spectra with excitation energy well-above the bandgap are qualitatively reproduced in terms of lifetimes of the intermediate states.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".