Compressed ultrafast tomographic imaging by passive spatiotemporal projections
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Bibliographic record
Abstract
Existing streak-camera-based two-dimensional (2D) ultrafast imaging techniques are limited by long acquisition time, the trade-off between spatial and temporal resolutions, and a reduced field of view. They also require additional components, customization, or active illumination. Here we develop compressed ultrafast tomographic imaging (CUTI), which passively records 2D transient events with a standard streak camera. By grafting the concept of computed tomography to the spatiotemporal domain, the operations of temporal shearing and spatiotemporal integration in a streak camera’s data acquisition can be equivalently expressed as the spatiotemporal projection of an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mo stretchy="false">(</mml:mo> <mml:mi>x</mml:mi> <mml:mo>,</mml:mo> <mml:mi>y</mml:mi> <mml:mo>,</mml:mo> <mml:mi>t</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:math> datacube from a certain angle. Aided by a new, to the best of our knowledge, compressed-sensing reconstruction algorithm, the 2D transient event can be accurately recovered in a few measurements. CUTI is exhibited as a new imaging mode universally adaptable to most streak cameras. Implemented in an image-converter streak camera, CUTI captures the sequential arrival of two spatially modulated ultrashort ultraviolet laser pulses at 0.5 trillion frames per second. Applied to a rotating-mirror streak camera, CUTI records an amination of fast-bouncing balls at 5,000 frames per second.
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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) | 0.000 | 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 it