Modeling the second stage of extended L-band fiber amplifiers using neural networks trained on experimental data
Bibliographic record
Abstract
Neural networks are fast and accurate in modeling L-band erbium-doped fiber amplifiers with a single stage. To extend this approach to second-stage (or mid-stage) amplifiers, we must address nonuniform and high-power input signals, as well as the presence of significant amplified spontaneous emission in the L-band. We present an experimental method to collect a large training set (15,000 points) for a neural network (NN) that can capture the behavior of gain and noise figure in second-stage amplifiers. We demonstrate that our neural network model has average error below 0.27 dB for gain, or 0.15 dB for noise figure. We examine strategies for collection of training sets, especially in terms of the granularity of the fiber lengths. To show the utility of the NN model as a design tool, we use it to optimize the mid-stage filter of a fixed double-stage amplifier through particle swarm optimization. We contrast mid-stage filters that target 1) flat gain and 2) low noise figure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".