Fast control of the transverse structure of a light beam using acousto-optic modulators
Bibliographic record
Abstract
Fast, reprogrammable control over the transverse structure of light beams plays an essential role in applications such as structured illumination microscopy, optical trapping, and quantum information processing. Existing technologies, such as liquid crystal on silicon spatial light modulators (LCoS-SLMs) and digital micromirror devices (DMDs), suffer from limited refresh rates, low damage thresholds, and high insertion loss. Acousto-optic modulators (AOMs) can resolve the above issues, as they typically handle higher laser power and offer lower insertion loss. By effectively mapping the temporal radio-frequency (RF) waveforms onto the spatial diffraction patterns of the optical field, individual AOMs have been shown to generate one-dimensional (1D) spatial modes at a pixel refresh rate of nearly 20 MHz. We extend this concept to enable fast modulation in a two-dimensional (2D) space using a double-AOM scheme. We demonstrate the generation of 2D Hermite-Gaussian (HG_nm) modes with an average fidelity of 81%, while the highest-order mode generated, HG_53, retains a fidelity of 56%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".