Compressed ultrafast tomographic imaging using standard streak cameras
Bibliographic record
Abstract
Streak cameras are popularly used to passively record dynamic events for numerous studies. However, in conventional operation, they are restricted to one-dimensional field of view (FOV) imaging. To overcome this limitation, the multipleshot and the single-shot two-dimensional (2D) steak imaging approaches have been developed. For the former, the (x,y,t) datacube is acquired by combining the conventional manipulation of streak cameras with a scanning operation. For the latter, the (x,y,t) information is obtained by combining streak imaging with other imaging strategies, such as compressed sensing (CS). Despite contributing to many new studies, the multiple-shot methods require a large number of measurements to synthesize the datacube, and the single-shot approaches reduce the spatiotemporal resolutions or the FOV. Here, we overcome these problems by developing streak-camera-based compressed ultrafast tomographic imaging (CUTI), which is a new work mode universally adaptable to most streak cameras. Grafting the principle of computed tomography to the spatiotemporal domain, CUTI uses temporal shearing and spatiotemporal integration to equivalently perform passive projections of a transient event. By leveraging multiple sweep ranges readily available in a standard streak camera and a new CS-based reconstruction algorithm, the datacube of the transient event can be accurately recovered using a few streak images. Compared to the scanning-based multiple-shot 2D streak imaging approaches, CUTI largely reduces the data acquisition time. Compared to the single-shot methods, CUTI eliminates the trade-off between the spatial resolution or the FOV and temporal resolution.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".