High-power and ultranarrow DFB laser: the effect of linewidth reduction systems on coherence length and interferometer noise
Bibliographic record
Abstract
In many sensing systems, a highly coherent laser source is necessary to perform sensitive interferometric or coherent measurements. At TeraXion, we have built a compact laser system that provides a stable laser frequency with a very narrow linewidth using a 60 mW DFB semiconductor laser. The linewidth reduction system uses a frequency discriminator to measure the laser frequency noise and provides an electrical feedback to reduce this noise over a given bandwidth. Experimental work shows that the phase noise of the DFB semiconductor laser can be reduced by more than 4 orders of magnitude from 10 Hz to 100 kHz. We analyzed the effect of the particular frequency noise spectrum of such a laser on its degree of coherence, its linewidth and the resulting interferometric noise. The laser linewidth computed from the power spectral density of frequency noise of the laser is reduced from 570 kHz down to an equivalent of 1.8 kHz when the output signal is observed for 30 ms, and from 370 kHz to 18 Hz for 1 ms. Similarly, the coherence length is increased from 145 m up to 45 km for fringes observed over 30 ms. Each result is compared with those obtained with a fiber laser.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".