Arbitrarily-shaped bursts of picosecond pulses from a fiber laser source for high-throughput applications
Bibliographic record
Abstract
Increasing the ablation efficiency of picosecond laser sources can be performed by bunching pulses in bursts1 and benefit from heat accumulation effects2-5 in the target. Pulsed fiber lasers are well suited for such a regime of operation, as the single pulse energy in a fiber is limited by the onset of nonlinear effects (SPM, SRS). Increasing the number of pulses to form a burst of pulses allows for average power scaling of picosecond fiber lasers. We are presenting in this paper a high-power fiber laser emitting arbitrarily-shaped bursts of picosecond pulses at 20 W of average output power. Burst duration can be varied from 2.5 ns to 80 ns. The burst repetition rate is externally triggered and can be varied from 100 kHz to 1 MHz. The single pulse duration is 60 ps and the repetition rate within a burst is 1.8 GHz. The output beam is linearly polarized (PER > 20 dB) and its M2 value is smaller than 1.15. The laser source has a tunable central wavelength around 1064 nm and a spectral linewidth compatible with frequency conversion. Conversion efficiency higher than 60% has been obtained at 10 W of 1064-nm output power.
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 | 0.000 | 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) | 0.002 | 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".