An auto-locked laser system for precision metrology and lidar applications
Bibliographic record
Abstract
We present a unique external cavity diode laser system that can be auto-locked with reference to atomic and molecular spectra. The vacuum-sealed laser head design uses an interchangeable base-plate comprised of a laser diode and optical elements that can be selected for desired wavelength ranges. The feedback light to the laser diode is provided by a narrow-band interference filter, which can be tuned from outside the laser cavity to fineadjust the output wavelength in vacuum. To stabilize the laser frequency, the digital laser controller relies either on a pattern-matching algorithm stored in memory, or on first or third derivative feedback. We have used the laser systems to perform spectroscopic studies in rubidium at 780 nm, and in iodine at 633 nm. The linewidth of the 780-nm laser system was measured to be ∼500 kHz, and we present Allan deviation measurements of the beat note and the lock stability. Furthermore, we show that the laser system can be the basis for a new class of lidar transmitters in which a temperature-stabilized fiber-Bragg grating is used to generate frequency references for on-line points of the transmitter. We show that the fiber-Bragg grating spectra can be calibrated with reference to atomic transitions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".