Ultrashort pulse identification based on a temporal-frequency dual-domain recognition algorithm
Bibliographic record
Abstract
The automatic identification of pulse types generated by ultrashort pulse lasers represents a critical advancement in laser science and precision measurement, facilitating the transition from "experience-driven" to "data-driven" intelligent laser systems. This study systematically investigates the dynamic characteristics of soliton pulses in passive mode-locked fiber lasers through comprehensive numerical simulations, focusing on the effects of intracavity net dispersion and saturable absorption energy on conventional solitons, self-similar pulses, dissipative solitons, and multiple solitons. To enhance the identification efficiency of different soliton types, we propose, to our knowledge, a novel temporal-frequency dual-domain recognition algorithm. The algorithm employs a two-stage approach: the first stage accurately determines the number of solitons based on temporal domain features, while the second stage classifies individual soliton types through spectral feature analysis. When applied to soliton identification across a two-dimensional parameter space, the algorithm successfully identifies 1435 sets of output solitons within 9 s, demonstrating exceptional speed and accuracy. This work not only advances our understanding of soliton distribution patterns in parameter space but also establishes a foundation for intelligent laser control and parameter optimization.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".