Stable, narrow-linewidth laser system with a broad frequency tunability and a fast switching time
Bibliographic record
Abstract
For a Rydberg atom-based sensor to change its sensing frequency, the wavelength of the Rydberg state excitation laser must be altered. The wavelength shifts required can be on the order of 10 nm. A fast-tunable narrow-linewidth laser with broadband tuning capability is required. Here, we present a demonstration of a laser system that can rapidly switch a coupling laser as much as 8 nm in less than 50 μs. The laser system comprises a frequency-stabilized continuous wave laser and an electro-optic frequency comb. A filter enables selection of individual comb lines. A high-speed electro-optic modulator is used to tune the selected comb line to a specific frequency, i.e., an atomic transition. Through Rydberg atom-based sensing experiments, we demonstrate frequency hopping between two Rydberg states and a fast switching time of 400 μs, which we show can be reduced to ∼50 μs with a ping-pong scheme. If updating the RF frequency is not required during frequency hopping, a 200 ns switching time can be achieved. These results showcase the potential of the laser system for advanced Rydberg atom-based radio frequency sensing applications, like communications and radar.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".