Polarization cross-talk from non-stationary sources of image jitter
Abstract
We generalize a previous formulation for the estimation of polarization cross-talk induced by atmospheric seeing to the case of image jitter that must be described as a non-stationary, and generally non-isotropic, random process. We demonstrate how the newly derived formalism converges towards the previous one in the limit of stationary random image jitter. In the case of non-stationary processes, we compare the predictions of the two formalisms, and illustrate the salient differences. For the sake of demonstration, we adopt for our analysis a specific example of power-spectral density derived from a circulant auto-correlation function, which describes a class of stationary random processes periodic over the length of the time series, and which we successively modified by the insertion of off-diagonal power spectral features at different frequencies in order to test the extended modeling framework for the estimation of polarization cross-talk.
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BibTeX
@misc{casini2026polarization,
title = {{Polarization cross-talk from non-stationary sources of image jitter}},
author = {R. Casini and A. G. de Wijn},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.08300},
eprint = {2611.08300},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.08300},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Polarization cross-talk from non-stationary sources of image jitter
AU - R. Casini
AU - A. G. de Wijn
PY - 2026
DA - 2026/11/05
PB - arXiv
DO - 10.48550/arxiv.2611.08300
UR - https://arxiv.org/abs/2611.08300
AB - We generalize a previous formulation for the estimation of polarization cross-talk induced by atmospheric seeing to the case of image jitter that must be described as a non-stationary, and generally non-isotropic, random process. We demonstrate how the newly derived formalism converges towards the previous one in the limit of stationary random image jitter. In the case of non-stationary processes, we compare the predictions of the two formalisms, and illustrate the salient differences. For the sake of demonstration, we adopt for our analysis a specific example of power-spectral density derived from a circulant auto-correlation function, which describes a class of stationary random processes periodic over the length of the time series, and which we successively modified by the insertion of off-diagonal power spectral features at different frequencies in order to test the extended modeling framework for the estimation of polarization cross-talk.
ER -