Excitonic analysis of many-body effects on the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mn>1</mml:mn><mml:mi>s</mml:mi></mml:mrow></mml:math>-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mn>2</mml:mn><mml:mi>p</mml:mi></mml:mrow></mml:math>intraband transition in semiconductor systems
Bibliographic record
Abstract
We present a detailed study of many-body effects associated with the intraband $1s$-$2p$ transition in two- and three-dimensional photoexcited semiconductors. We employ a previously developed excitonic model to treat effects of exchange and phase space filling (PSF). In this work, we extend the model to include intraband transitions and static free-carrier screening. The exciton transition energies are renormalized by many-body interactions, and the excitonic dynamical equations provide simple expressions for the individual contributions of screening, PSF and exchange. The excitonic model correctly predicts the blue shift and bleaching of the $1s$ exciton resonance due to exchange and PSF. Free-carrier screening is found to enhance these effects by lowering the binding energy of the $1s$ exciton. In contrast, the effects of free-carrier screening on the $1s$-$2p$ transition energy are subtler. For a coherent exciton system, in the absence of free-carrier screening, exchange and PSF lead to a blue shift of the transition energy. However, screening decreases the $1s$ binding energy faster than the $2p$ binding energy, which in turn decreases the transition energy. Thus screening effects oppose exchange and PSF, and the overall magnitude and sign of the $1s$-$2p$ transition energy shift depends on the free-carrier density. Specifically, for low to moderate excitation densities, exchange and PSF can be dominated by screening, leading to a net redshift of the transition energy. The results for two- and three-dimensional systems are qualitatively similar, although the magnitude of the shift is much smaller in three dimensions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".