Single-shot ultrafast terahertz photography
Bibliographic record
Abstract
Multidimensional imaging of transient events has proven pivotal in unveiling many fundamental mechanisms in physics, chemistry, and biology. In particular, real-time imaging modalities with ultrahigh temporal resolutions are required for capturing ultrashort events on picosecond timescales. Despite recent approaches witnessing a dramatic boost in high-speed photography, current single-shot ultrafast imaging schemes operate only at conventional optical wavelengths, being suitable solely within an optically-transparent framework. Here, leveraging on the unique penetration capability of terahertz radiation, we demonstrate a single-shot ultrafast terahertz photography system that can capture multiple frames of a complex ultrafast scene in non-transparent media with sub-picosecond temporal resolution. By multiplexing an optical probe beam in both the time and spatial-frequency domains, we encode the terahertz-captured three-dimensional dynamics into distinct spatial-frequency regions of a superimposed optical image, which is then computationally decoded and reconstructed. Our approach opens up the investigation of non-repeatable or destructive events that occur in optically-opaque scenarios.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".