Optical turbulence distorts beam amplitude and phase, causing spreading, wandering, and irradiance fluctuations. Reconstructing perturbed beams’ complex fields is experimentally challenging due to these dynamic effects. Our complex phase retrieval technique, using binary amplitude modulation with a DMD and high-speed camera, characterizes collimated beams through turbulence and overcomes interferometric limitations. Conventionally, phase retrieval modulates optical fields via random coded apertures (RCA) to recover amplitude and phase without prior knowledge, solving ill-posed problems with phase-lift algorithms. Our previous approach required ≥20 apertures, increasing acquisition time and complexity. We designed a new coded aperture, reducing time and enhancing quality over traditional RCA. Then we apply a novel deep-learning phase unwrapping algorithm enabling efficient unwrapping of phases with turbulence-induced branch point singularities manifesting as vortices. This is the first experimental observation of turbulence complex wavefronts reconstructed with high spatial resolution and sampling rate. We discuss observed statistical properties and compare with current models.
The new generation of extremely large telescope (ELT) introduces many challenges in optics and engineering. A key challenge is the development of an adaptive optics system able to handle elongated laser guide star (ELGS). Classic wavefront sensor (WFS), such as the shack-hartmann wavefront sensor (SHWFS) or pyramidal wavefront sensor (PyWFS), are not able to readily handle elongated stars, which gets worse when the atmospheric turbulence becomes stronger. In this work, we present a novel complex field wavefront sensor (CFWFS) that can reconstruct the phase and amplitude of the extended bodies at the image plane, and then it is able to recover the turbulent phase at the pupil plane. The proposed WFS scheme uses a four times faster parallel phase retrieval algorithm with only eight designed coded aperture (DCA) that is designed using sphere packing coded apertures (SPCA). We present a collection of encouraging preliminary simulation results.
Any beam that propagates through optical turbulence will experience distortions in both its amplitude and phase, leading to various effects such as beam wandering, beam spreading, and irradiance fluctuations. Reconstructing the complete field of a perturbed beam is a challenging task due to the dynamic nature of these effects. Interferometric wavefront reconstruction techniques—such as those based on holography—are commonly used but are hindered by their sensitivity to environmental disturbances and alignment errors. However, new complex phase retrieval methods based on propagation equations have emerged, which do not require prior knowledge of the beam to be reconstructed and are suitable for amplitude or phase objects, or both. We propose an experimental implementation of a complex phase retrieval technique for characterizing Gaussian beams propagating through optical turbulence, using binary amplitude modulation with a digital micro-mirror device (DMD). This approach is ideal for dynamic applications and has enabled us to achieve experimental high-speed complex wavefront reconstruction of optical beams through controlled real turbulence. This experiment corresponds to the initial step in our research focused on gaining a deeper understanding of optical turbulence from an experimental perspective.
In the last decade, a nascent trend of characterizing turbulence from observing features of distant targets through ground-layer turbulence have been relentless growing. Either from observing regular geometrical features of buildings or arrays of LEDs, it is possible to retrieve the structure constant of the refractive index fluctuations. On the other hand, because of the lack of a definitive theoretical model describing anisotropic or inhomogeneous turbulence, most experimental observations have been reduced to mere descriptions in the event of deviations from expected Obukhov-Kolmogorov predictions. Our group has been able to retrieve power-spectrum exponents, without a prior knowledge of a subjacent model, and henceforth determine anisotropic behavior in controlled optical turbulence; furthermore, under convective turbulence, an exponent can be obtained from time series of the occurrence of power drops in optical communication links: extreme events.
In this manuscript, we present a technique identifying as extreme events sudden changes in morphological characteristics of an array of point sources observed through real controlled anisotropic turbulence assisted by a deep-learning ad-hoc. This approach provides an effective approach to reduce high-volume data from imaging targets into a real-time stream of parameters to fully characterize optical turbulence.
We present the design and implementation of an adaptive optics test bench recently built at the School of Electrical Engineering of the Pontificia Universidad Católica de Valparaíso in Chile. The flexible design of the PULPOS bench incorporates state-of-the-art, high-speed spatial light modulators for atmospheric turbulence emulation and wavefront correction, a deformable mirror for modulation, and a variety of wavefront sensors such as a pyramid wavefront sensor. PULPOS serves as a platform for research on adaptive optics and wavefront reconstruction using artificial intelligence techniques, as well as for educational purposes.
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