Spatiotemporal walk-off and improved focusing of plasma THz sources
Bibliographic record
Abstract
High-field THz sources with peak field strengths exceeding MV/cm are essential for nonlinear THz spectroscopy and coherent control of matter on ultrafast time scales. Two-color femtosecond laser plasma sources employing long filamentation have been reported as providing single-cycle, >MV/cm fields, with multi-decade spanning bandwidth and polarization control, making them promising sources for such experiments. In this work, we report the observation of spatiotemporal spreading of the THz pulse when standard off-axis parabolic mirrors are used for collection and focusing of long filament plasma-based THz pulses. This produces a flying focus for THz light, with the axial focal region propagating at a velocity of 1/3 the speed of light. The THz emission is then subsequently spread over a temporal width of ∼10 ps, approximately 100 times the THz pulse duration detected by electro-optic sampling at any single point along the focus. The consequences of this non-ideal focusing are a potential and drastic overestimation of the peak THz electric field based on energy measurements, as well as significant phase noise arising from beam pointing fluctuations. We show that this spatiotemporal spreading can be minimized using a simple axicon lens that perfectly collimates the extended filament source, resulting in improved spatial and temporal focusing of the THz pulse.
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) | 0.001 | 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".