Effect of strong correlations on the high energy anomaly in hole- and electron-doped high-<i>T</i><sub>c</sub>superconductors
Bibliographic record
Abstract
Recently, angle-resolved photoemission spectroscopy (ARPES) has been used to highlight an anomalously large band renormalization at high binding energies in cuprate superconductors: the high energy 'waterfall' or high energy anomaly (HEA). This paper demonstrates, using a combination of new ARPES measurements and quantum Monte Carlo simulations, that the HEA is not simply the by-product of matrix element effects, but rather represents a cross-over from a quasi-particle band at low binding energies near the Fermi level to valence bands at higher binding energy, assumed to be of strong oxygen character, in both hole- and electron-doped cuprates. While photoemission matrix elements clearly play a role in changing the aesthetic appearance of the band dispersion, i.e. the 'waterfall'-like behavior, they provide an inadequate description for the physics that underlies the strong band renormalization giving rise to the HEA. Model calculations of the single-band Hubbard Hamiltonian showcase the role played by correlations in the formation of the HEA and uncover significant differences in the HEA energy scale for hole- and electron-doped cuprates. In addition, this approach properly captures the transfer of spectral weight accompanying both hole and electron doping in a correlated material and provides a unifying description of the HEA across both sides of the cuprate phase diagram.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".