Single-shot real-time femtosecond imaging of temporal focusing
Bibliographic record
Abstract
While the concept of focusing usually applies to the spatial domain, it is equally applicable to the time domain. Real-time imaging of temporal focusing of single ultrashort laser pulses is of great significance in exploring the physics of the space-time duality and finding diverse applications. The drastic changes in the width and intensity of an ultrashort laser pulse during temporal focusing impose a requirement for femtosecond-level exposure to capture the instantaneous light patterns generated in this exquisite phenomenon. Thus far, established ultrafast imaging techniques either struggle to reach the desired exposure time or require repeatable measurements. We have developed single-shot 10-trillion-frame-per-second compressed ultrafast photography (T-CUP), which passively captures dynamic events with 100-fs frame intervals in a single camera exposure. The synergy between compressed sensing and the Radon transformation empowers T-CUP to significantly reduce the number of projections needed for reconstructing a high-quality three-dimensional spatiotemporal datacube. As the only currently available real-time, passive imaging modality with a femtosecond exposure time, T-CUP was used to record the first-ever movie of non-repeatable temporal focusing of a single ultrashort laser pulse in a dynamic scattering medium. T-CUP's unprecedented ability to clearly reveal the complex evolution in the shape, intensity, and width of a temporally focused pulse in a single measurement paves the way for single-shot characterization of ultrashort pulses, experimental investigation of nonlinear light-matter interactions, and real-time wavefront engineering for deep-tissue light focusing.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".