Optimization of an All Normal Dispersion Fiber Laser and a Gain Managed Nonlinear Amplifier
Bibliographic record
Abstract
Ultrafast laser systems are used in a wide variety of modern laser research. The combination \nof an all-normal dispersion fiber laser and a gain-managed nonlinear fiber amplifier makes for \ninexpensive and easy to build system that can generate ultrashort pulses with high average \npower. In this thesis I explore the improvements and optimizations made to such a system \nfor use in making a two-color laser amplifier system, to be used for projects such as multi frequency Raman generation. An all-normal dispersion fiber mode-locked laser was developed \nfor our group, but modifications were necessary to improve both the ease of mode-locking and \nextend the duration of self-sustaining. Spectral filtering is the key aspect of the mode-locking \noperations of an all-normal dispersion fiber laser and it is the mode-locking that generates the \nultrashort pulses. This spectral filtering was optimized to improve the ease of mode-locking. \nThe pulses at the output of the mode-locked laser were found to be too long to allow the \nmaximum spectral broadening in the gain-managed nonlinear amplifier. Compression of these \npulses with a grating compressor caused the amplified spectrum to be significantly broadened \nby the nonlinear optical interaction in the fiber. The resulting spectra of the nonlinear amplifier \nwere analyzed as a function of seed power and pump power (up to an upper limit before the \nintroduction of incoherent noise that seeds Raman scattering creating a red shoulder on the \nspectrum). The result of these investigations is an optimized laser system that produces a train \nof pulses with energy of 176nJ, a bandwidth exceeding 100nm, and an uncompressed pulse \nduration of approximately 6ps. The system can now deliver the needed energy and bandwidth \nfor the two-color amplification experiments that will be conducted in the future with this laser \nsystem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".