Many-impurity effects in Fourier transform scanning tunneling spectroscopy
Bibliographic record
Abstract
Fourier transform scanning tunneling spectroscopy (FTSTS) is a useful technique for extracting details of the momentum-resolved electronic band structure from inhomogeneities in the local density of states due to disorder-related quasiparticle scattering. To a large extent, current understanding of FTSTS is based on models of Friedel oscillations near isolated impurities. Here, a framework for understanding many-impurity effects is developed based on a systematic treatment of the variance $\ensuremath{\Delta}{\ensuremath{\rho}}^{2}(\mathbf{q},\ensuremath{\omega})$ of the Fourier transformed local density of states $\ensuremath{\rho}(\mathbf{q},\ensuremath{\omega})$. One important consequence of this work is a demonstration that the poor signal-to-noise ratio inherent in $\ensuremath{\rho}(\mathbf{q},\ensuremath{\omega})$ due to randomness in impurity positions can be eliminated by configuration averaging $\ensuremath{\Delta}{\ensuremath{\rho}}^{2}(\mathbf{q},\ensuremath{\omega})$. Furthermore, we develop a diagrammatic perturbation theory for $\ensuremath{\Delta}{\ensuremath{\rho}}^{2}(\mathbf{q},\ensuremath{\omega})$ and show that an important bulk quantity, the mean-free-path, can be extracted from FTSTS experiments.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".