Plasmon mode engineering with electrons on helium
Bibliographic record
Abstract
An ensemble of electrons trapped above superfluid helium offers a paradigm system for investigating and controlling collective charge dynamics in low-dimensional electronic matter. Of particular interest is the ability to spatially control and engineer surface plasmons for integration with hybrid quantum systems and circuit quantum electrodynamic device architectures. Here we present experiments using an electron-on-helium microchannel device that hosts microwave-frequency plasmons, generated via local microwave excitation in an electrostatically defined central channel. By precisely varying the electron density, we demonstrate tunability of plasmon mode frequencies over several GHz. Additionally, we find that the power dependence of these modes can be used to investigate both homogeneous and inhomogeneous sources of spectral broadening. These results demonstrate the versatility of electrons on helium for probing collective excitations in low-dimensional Coulomb liquids and solids, and demonstrate a path for integrating engineered plasmons in electrons on helium with hybrid circuit quantum electrodynamic systems. Trapping electrons on a superfluid helium surface provides access to collective quantum phenomena and a platform for circuit quantum electrodynamics (cQED). Here, the authors demonstrate precision spatial and frequency engineering of plasmonic modes in a hybrid electron-on-helium system, opening the door towards integration of plasmon physics within future cQED-like devices.
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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 | 0.000 | 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) | 0.001 | 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".