Fiber Laser Pump Reflector Based on Volume Bragg Grating
Bibliographic record
Abstract
Double-clad rare-earth doped fiber lasers have been the subject of numerous studies due to their high-quality single-mode beam and overall simplicity partly arising from their cladding-pumped scheme. This approach reduces, however, greatly the pump absorption, which leads to longer laser cavity lengths which are inherently more lossy and costly. There is therefore a strong incentive in increasing the pump absorption while reducing laser cavity length. It was shown that a Long-Period Fiber Grating (LPG) could be used to couple the pump beam propagating in the cladding of the doped fiber to its core which increased the pump absorption by 35% [1]. Another approach consisted in having an all-fiber pump reflector. A reflectivity of 55% of the unabsorbed pump was reported by giving a right-angled conical shape to the fiber end [2]. Also, a pump reflector with a 46% reflectivity was obtained by writing a FBG with UV radiation directly in the photosensitive germanosilicate hydrogen-loaded cladding of a specialty fiber [3]. In this contribution, we report the writing of a volume Bragg grating (VBG) written directly in the pure-silica cladding of an erbium-doped fiber acting as a pump reflector for an all-fiber laser cavity. Such VBG was written using femtosecond pulses at 400 nm and the phase-mask writing technique and yields a peak reflectivity of 71% of the unabsorbed pump power of a 976-nm diode propagating in the cladding.
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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 | 0.000 |
| 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".