Through robotic small-tool polishing, the RMS surface figure of a 100-mm flat mirror was converged to 1788 nm. The robotic method also produced a 0008 nm convergence for a 300-mm high-gradient ellipsoid mirror, eliminating the need for any manual participation. see more A 30% improvement in polishing efficiency was achieved relative to manual polishing. Insights gleaned from the proposed SCP model will facilitate progress in subaperture polishing techniques.
Surface defects on mechanically machined fused silica optical surfaces host a concentration of point defects with varied species, resulting in a sharp decline in laser damage resistance under substantial laser irradiation. A material's capacity to resist laser damage is influenced by the unique roles of different point defects. A key unknown in understanding the inherent quantitative relationship among diverse point defects lies in the lack of determination of their relative proportions. A comprehensive understanding of the comprehensive effect of diverse point imperfections necessitates a systematic analysis of their origins, development patterns, and especially the quantitative interrelationships among them. Seven varieties of point defects were determined through this investigation. Laser damage is induced by the ionization of unbonded electrons in point defects, a phenomenon correlated to the relative abundance of oxygen-deficient and peroxide point defects. Scrutinizing the photoluminescence (PL) emission spectra and the properties of point defects (e.g., reaction rules and structural features) offers further confirmation of the conclusions. By combining fitted Gaussian components with electronic transition theory, a quantitative correlation linking photoluminescence (PL) to the proportions of diverse point defects is derived for the first time. When considering the proportion of the accounts, E'-Center is the dominant one. By comprehensively revealing the action mechanisms of various point defects, this research offers novel perspectives on understanding defect-induced laser damage mechanisms in optical components under intense laser irradiation, specifically at the atomic scale.
Fiber specklegram sensors, unlike many other sensing technologies, circumvent intricate fabrication procedures and costly interrogation methods, offering an alternative to conventional fiber optic sensing. Reported specklegram demodulation techniques, frequently employing correlation calculations based on statistical properties or feature classifications, frequently suffer from limited measurement range and resolution. This paper details a learning-enabled, spatially resolved approach to sensing fiber specklegram bending. The evolution of speckle patterns can be learned by this method, which employs a hybrid framework. This framework, composed of a data dimension reduction algorithm and a regression neural network, accurately identifies curvature and perturbed positions from the specklegram, even for previously unobserved curvature configurations. Rigorous experimentation was undertaken to validate the proposed method's practicality and resilience. Prediction accuracy for the perturbed position was 100%, with average prediction errors of 7.791 x 10⁻⁴ m⁻¹ and 7.021 x 10⁻² m⁻¹ for learned and unlearned configuration curvatures, respectively. Fiber specklegram sensors find expanded practical applications through this method, which offers deep learning-based insights for the analysis of sensing signals.
Chalcogenide hollow-core anti-resonant fibers (HC-ARFs) are a potentially excellent choice for the delivery of high-power mid-infrared (3-5µm) lasers, but the need for better comprehension of their properties and improvements in their fabrication processes is undeniable. A seven-hole chalcogenide HC-ARF, featuring integrated cladding capillaries, is presented in this paper, its fabrication achieved using a combination of the stack-and-draw method and dual gas path pressure control, employing purified As40S60 glass. We theoretically predict and experimentally verify that the medium possesses a superior ability to suppress higher-order modes, displaying several low-loss transmission bands in the mid-infrared spectrum. The measured fiber loss at 479 µm reached a minimum of 129 dB/m. Our findings have implications for the fabrication and practical use of various chalcogenide HC-ARFs in mid-infrared laser delivery systems.
Miniaturized imaging spectrometers struggle with bottlenecks that impede the reconstruction of their high-resolution spectral images. Within this study, a zinc oxide (ZnO) nematic liquid crystal (LC) microlens array (MLA) was leveraged to develop an optoelectronic hybrid neural network. This architecture optimizes the neural network's parameters, taking full advantage of the ZnO LC MLA, by implementing the TV-L1-L2 objective function with mean square error as the loss function. Optical convolution, facilitated by the ZnO LC-MLA, serves to reduce the network's volume. Within a relatively brief period, experimental outcomes showed the proposed architectural method effectively reconstructed a 1536×1536 pixel resolution enhanced hyperspectral image, covering the wavelength range of 400nm to 700nm. Results indicated a spectral accuracy of 1nm during the reconstruction.
The rotational Doppler effect (RDE) is a subject of considerable research interest, permeating disciplines ranging from acoustics to optics. RDE's detection strongly correlates with the orbital angular momentum of the probe beam; meanwhile, the recognition of radial mode is ambiguous. Based on complete Laguerre-Gaussian (LG) modes, we expose the mechanism of interaction between probe beams and rotating objects, shedding light on the role of radial modes in RDE detection. Through both theoretical and experimental means, the significance of radial LG modes in RDE observation is apparent, arising from the topological spectroscopic orthogonality between probe beams and objects. Through the application of multiple radial LG modes, we improve the probe beam, resulting in RDE detection highly sensitive to objects showcasing intricate radial structures. Additionally, a novel method for estimating the performance of various probe beams is suggested. see more This work's implications extend to the transformation of RDE detection methods, thereby positioning corresponding applications on a higher technological platform.
This work details the measurement and modeling of tilted x-ray refractive lenses, focusing on their x-ray beam effects. The modelling's accuracy is validated by comparing it to metrology data from x-ray speckle vector tracking (XSVT) experiments conducted at the BM05 beamline of the ESRF-EBS light source; the results show a high degree of concordance. We are permitted by this validation to investigate and explore potential implementations of tilted x-ray lenses in optical design. While the tilting of 2D lenses lacks apparent appeal in the context of aberration-free focusing, the tilting of 1D lenses about their focusing axis can offer a means of smoothly refining their focal length. Through experimental means, we illustrate the continuous modulation of the apparent lens radius of curvature, R, achieving reductions up to two-fold and beyond; potential applications within beamline optical design are subsequently discussed.
To understand the radiative forcing and climate impacts of aerosols, it is essential to examine their microphysical characteristics, such as volume concentration (VC) and effective radius (ER). While remote sensing offers valuable data, resolving aerosol vertical profiles (VC and ER) based on range remains unattainable currently, with only sun-photometer observations providing integrated columnar information. A pioneering retrieval technique for range-resolved aerosol vertical columns (VC) and extinctions (ER) is presented in this study, combining partial least squares regression (PLSR) and deep neural networks (DNN) with the integration of polarization lidar and collocated AERONET (AErosol RObotic NETwork) sun-photometer observations. Measurements made with widespread polarization lidar successfully predict aerosol VC and ER, with correlation (R²) reaching 0.89 for VC and 0.77 for ER when using the DNN method, as illustrated by the results. The height-resolved vertical velocity (VC) and extinction ratio (ER) data obtained by the lidar near the surface are validated by the independent measurements from the collocated Aerodynamic Particle Sizer (APS). Variations in atmospheric aerosol VC and ER, both daily and seasonal, were prominent findings at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL). In comparison to the columnar measurements from sun-photometers, this study demonstrates a reliable and practical method for determining full-day range-resolved aerosol volume concentration and extinction ratio using routinely employed polarization lidar observations, even under cloudy circumstances. The present study's methodology can also be utilized with current ground-based lidar networks and the CALIPSO satellite lidar to perform long-term observations, with the objective of assessing aerosol climatic effects with greater precision.
Single-photon imaging technology, boasting picosecond resolution and single-photon sensitivity, stands as an ideal solution for ultra-long-distance imaging in extreme environments. Despite advancements, current single-photon imaging technology struggles with slow imaging speeds and low-quality images, resulting from the impacts of quantum shot noise and fluctuating background noise. This work introduces a highly efficient single-photon compressed sensing imaging technique, employing a novel mask designed through the integration of Principal Component Analysis and Bit-plane Decomposition algorithms. High-quality single-photon compressed sensing imaging with diverse average photon counts is achieved by optimizing the number of masks, accounting for the effects of quantum shot noise and dark counts in the imaging process. Improvements in both imaging speed and quality are substantial when compared to the usual Hadamard procedure. see more A 6464-pixel image was captured in the experiment through the utilization of only 50 masks, leading to a 122% compression rate in sampling and an 81-fold acceleration of sampling speed.