Infrared imaging at hypersonic speeds: assessing sub-microsecond exposure
Bibliographic record
Abstract
Acquiring clear radiometric imagery of high velocity targets requires perfect synchronization, an ultra-short exposure time (ET), and good signal to noise ratio (SNR). Recent demand for advanced infrared (IR) imaging capabilities optimized for hypersonic applications necessitates the exploration of existing IR imaging systems capable of accessing sub-microsecond exposures. Currently, top performing commercial off-the-shelf (COTS) IR cameras are not designed to reach such exposures, however some are not necessarily unable. Traditionally, even when hardware may be capable, these exposures are left inaccessible by the system firmware as a design choice, as pushing the boundary of low ET necessitates a tradeoff between key imaging requirements and design choices which would not be well suited for traditional scientific IR imaging. This study investigates the feasibility of unlocking sub-microsecond exposure on currently produced cooled infrared scientific imaging instruments produced by Telops (Québec, Canada). Our research includes a comprehensive evaluation of the systems’ capabilities, and performance under various expected conditions simulating hypersonic test environments. Key metrics including expected signal levels, noise, temperature calibration range, and well depth will be addressed and analyzed alongside a study of the systems’ intrinsic performance capabilities. Finally, this study will outline the technical challenges expected, solutions proposed, and the implications of our results for future development. Ultimately, the culmination of these findings suggests that current IR camera technology holds the potential to be optimized for hypersonic research and paves the way for imminent development of such tools.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".