Impact of electron-phonon coupling on near-field optical spectra in graphene
Bibliographic record
Abstract
The finite momentum transfer $\mathbit{q}$ longitudinal optical response ${\ensuremath{\sigma}}^{L}(\mathbit{q},\ensuremath{\omega})$ of graphene has a peak at an energy $\ensuremath{\omega}=\ensuremath{\hbar}{v}_{F}q$. This corresponds directly to a quasiparticle peak in the spectral density at a momentum relative to the Fermi momentum ${k}_{F}\ensuremath{-}q$. Inclusion of coupling to a phonon mode at ${\ensuremath{\omega}}_{E}$ results, for $\ensuremath{\omega}<|{\ensuremath{\omega}}_{E}|$, in an electron-phonon renormalization of the bare bands by a mass enhancement factor $(1+\ensuremath{\lambda})$, and this is followed by a phonon kink for $\ensuremath{\omega}$ around ${\ensuremath{\omega}}_{E}$ where additional broadening begins. Here we study the corresponding changes in the optical quasiparticle peaks, which we find continue to track directly the renormalized quasiparticle energies until $q$ is large enough that the optical transitions begin to sample the phonon kink region of the dispersion curves, where linearity in momentum and the correspondence to a single-quasi-particle energy are lost. Nevertheless there remain in ${\ensuremath{\sigma}}^{L}(\mathbit{q},\ensuremath{\omega})$ features analogous to the phonon kinks of the dispersion curves which are observable through variation of $q$ and $\ensuremath{\omega}$.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".