Fringe visibility for pulsed digital holography with large optical path differences
Bibliographic record
Abstract
In interferometry and holography systems, the optical path difference (OPD) between the beams must be kept smaller than the source coherence length to obtain stable interference fringes. When the OPD is larger than the coherence length, the fringe phase drifts on time scales comparable to the source coherence time. This reduces fringe visibility and causes fringes to wash out completely for observation times longer than the coherence time. High fringe visibility measurements, however, can be obtained with pulsed sources (or very short detector integration times), if the pulse duration is shorter than the source coherence time, thereby limiting the amount of fringe phase drift over the pulse duration. We analyze cases where optical pulses are carved from a continuous-wave laser source. We derive an expression for the squared magnitude of the fringe visibility for pulsed interference measurements and demonstrate that high visibility fringes can be obtained even when the OPD is much longer than the source coherence length. As a rule of thumb, the source coherence time should be at least 10 times greater than the pulse duration to obtain expected fringe visibilities greater than 95% when the source laser has a Lorentzian line shape. This result is important to the design of digital holography systems for long-range remote sensing applications, where it is difficult to implement a dynamic optical delay line for path length matching with non-cooperative objects.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".