Unfolding the kagome lattice to improve understanding of ARPES in CoSn
Bibliographic record
Abstract
Metallic kagome lattices are attracting significant attention as they provide a platform to explore the interplay between topology and magnetism. Angle-resolved photoemission spectroscopy (ARPES) plays a key role in unraveling their electronic structure. However, the analysis is often challenging due to the presence of multiple bands near the Fermi level. Indeed, each orbital generates three bands in a kagome lattice due to its three sites motif, which soon becomes complicated if many orbitals are present. To address this complexity, using ARPES matrix elements can be highly beneficial. First, band symmetry can be determined through selection rules based on light polarization. We emphasize that, in kagome lattices, as in all multisite lattices, symmetry of the Bloch state is not only determined by the orbital character but also by the relative phase between the three sublattices. Additionally, interference between the three sublattices leads to a strong modulation of ARPES intensity across neighboring Brillouin zones. We show how unfolded band calculations capture these modulations, helping with band identification. We apply these ideas to CoSn, whose simple structure retains the key features of a kagome lattice. Using polarization dependent ARPES in several Brillouin zones, we isolate the dispersion of each band and discuss novel correlation effects, selectively renormalizing the bands crossing the Fermi level and shifting the others.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".