Superoscillations without Sidebands: Power-Efficient Sub-Diffraction Imaging with Propagating Waves
Bibliographic record
Abstract
A superoscillation wave is a special superposition of propagating electromagnetic (EM) waves which varies with sub-diffraction resolution inside a fixed region. This special property allows superoscillation waves to carry sub-diffraction details of an object into the far-field, and makes it an attractive candidate technology for super-resolution devices. However, the Shannon limit seemingly requires that superoscillations must exist alongside high-energy sidebands, which can impede its widespread application. In this work we show that, contrary to prior understanding, one can selectively synthesize a portion of a superoscillation wave and thereby remove its high-energy region. Moreover, we show that by removing the high-energy region of a superoscillation wave-based imaging device, one can increase its power efficiency by two orders of magnitude. We describe the concept behind this development, elucidate conditions under which this phenomenon occurs, then report fullwave simulations which demonstrate the successful, power-efficient generation of sub-wavelength focal spots from propagating waves.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".