Polarimetric LiDAR backscattering contrast of linearly and circularly polarized pulses for ideal depolarizing targets in generic water fogs
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Bibliographic record
Abstract
In this paper, we investigate the backscattering depolarization of linearly and circularly polarized laser sources propagating in dense water fogs. We limit our investigation to a simple case where an active LiDAR system is pointed toward a white depolarizing Lambertian solid target. The receiver captures the reflected signal in the orthogonal channel so as to remove most of the backscattering from the water fog. It is shown that in the studied cases, a circularly polarized signal is depolarized faster than a linearly polarized signal and thus produces less contrast. We show that in the cases that can be described by the small angle approximation, the Rubenson degree of polarization (DoP) of a circularly polarized beam can be predicted by the DoP of a linearly polarized beam as <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mtext>DoP</mml:mtext> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mtext>cir</mml:mtext> </mml:mrow> </mml:msub> </mml:mrow> <mml:mo>=</mml:mo> <mml:mn>2</mml:mn> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mtext>DoP</mml:mtext> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mtext>lin</mml:mtext> </mml:mrow> </mml:msub> </mml:mrow> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> </mml:math> , even for low-order multiple scattering events. In these conditions, since the linear DoP is always stronger, the contrast is expected to be better in linear polarization for ideal depolarizing targets.
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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 | 0.001 |
| 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)
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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