The 100-mm flat mirror's surface figure root mean square (RMS) achieved a convergence of 1788 nm solely via robotic small-tool polishing, without any human input. Likewise, the 300-mm high-gradient ellipsoid mirror converged to 0008 nm through the same automated polishing process, dispensing with manual assistance. BMS309403 There was a 30% improvement in polishing efficiency, surpassing manual polishing techniques. 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. The susceptibility to laser damage is directly correlated with the specific functions of varied 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. To fully determine the wide-ranging effect of different point defects, a thorough investigation into their origins, the principles governing their evolution, and especially the quantitative connections among them is indispensable. The investigation into point defects yielded seven categories. The ionization of unbonded electrons in point defects is observed to be a causative factor in laser damage occurrences; a quantifiable relationship is present between the proportions of oxygen-deficient and peroxide point defects. The photoluminescence (PL) emission spectra and the characteristics of point defects, including their reaction rules and structural attributes, provide additional support for the conclusions. A novel quantitative relationship between photoluminescence (PL) and the concentrations of various point defects is formulated, for the first time, leveraging the fitted Gaussian components and electronic transition principles. The E'-Center category represents the most significant portion of the total. This investigation into the comprehensive action mechanisms of diverse point defects, provides groundbreaking insights into defect-induced laser damage mechanisms in optical components under intense laser irradiation, analyzed from an atomic perspective.

Fiber specklegram sensors, avoiding the complexities of traditional fabrication and interrogation schemes, offer a cost-effective and less intricate alternative to currently utilized fiber optic sensing technologies. Most specklegram demodulation schemes reported, which leverage correlation calculations grounded in statistical properties or feature classifications, are constrained in their measurement ranges and resolutions. Our work introduces and validates a spatially resolved method for fiber specklegram bending sensors, empowered by machine learning. By constructing a hybrid framework that intertwines a data dimension reduction algorithm with a regression neural network, this method can grasp the evolutionary process of speckle patterns. The framework simultaneously gauges curvature and perturbed positions from the specklegram, even when the curvature isn't part of the training data. Experimental validation of the proposed scheme's practicality and robustness revealed a perfect prediction accuracy for the perturbed position. Average prediction errors for the curvature of the learned and unlearned configurations were 7.791 x 10⁻⁴ m⁻¹ and 7.021 x 10⁻² m⁻¹, respectively. By employing deep learning, this method facilitates practical applications for fiber specklegram sensors, providing valuable perspectives on the interrogation of sensing signals.

Anti-resonant chalcogenide hollow-core fibers (HC-ARFs) show promise in delivering high-power mid-infrared (3-5µm) lasers, despite the limited understanding of their characteristics and the challenges in their manufacturing process. We present, in this paper, a seven-hole chalcogenide HC-ARF with touching cladding capillaries, manufactured from purified As40S60 glass, using the stack-and-draw method combined with dual gas path pressure control. 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 research outcomes enable the fabrication and implementation of various chalcogenide HC-ARFs, thereby contributing to mid-infrared laser delivery system advancement.

Miniaturized imaging spectrometers encounter obstacles in the process of reconstructing high-resolution spectral images. In this investigation, a novel optoelectronic hybrid neural network design was presented, incorporating a zinc oxide (ZnO) nematic liquid crystal (LC) microlens array (MLA). 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. To shrink the network's footprint, the ZnO LC-MLA is leveraged for optical convolution. 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.

Across a spectrum of research disciplines, from acoustics to optics, the rotational Doppler effect (RDE) commands substantial attention. The orbital angular momentum of the probe beam is largely responsible for observing RDE, though the impression of radial mode remains uncertain. To understand the role of radial modes in RDE detection, we disclose the interaction process between probe beams and rotating objects, drawing upon complete Laguerre-Gaussian (LG) modes. Radial LG modes play a vital role in the observation of RDE, as evidenced through theoretical and experimental methods; this is attributed to the topological spectroscopic orthogonality between probe beams and objects. Employing multiple radial LG modes elevates the sensitivity of RDE detection to objects with sophisticated radial structures, augmenting the probe beam. Along with this, a particular method of estimating the efficiency of a wide array of probe beams is detailed. BMS309403 Through this work, there is potential for modification of the RDE detection method, and related applications will be elevated to a novel platform.

This work details the measurement and modeling of tilted x-ray refractive lenses, focusing on their x-ray beam effects. Benchmarking the modelling against x-ray speckle vector tracking (XSVT) metrology obtained at the ESRF-EBS light source's BM05 beamline yields very good results. The validation process facilitates our exploration of the potential applications of tilted x-ray lenses within optical design methodologies. 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. Empirical investigation reveals a persistent alteration in the perceived lens radius of curvature, R, wherein reductions of up to twice, or more, are attained; this finding opens avenues for applications in beamline optical engineering.

Climate change impacts and radiative forcing from aerosols are significantly influenced by their microphysical properties, including volume concentration (VC) and effective radius (ER). Although remote sensing has progressed, detailed aerosol vertical profiles, VC and ER, are not obtainable through range resolution, and only the integrated column from sun-photometer readings is currently accessible. This study initially proposes a method for range-resolved aerosol vertical column (VC) and extinction (ER) retrieval, blending partial least squares regression (PLSR) and deep neural networks (DNN) with data from polarization lidar and coincident AERONET (AErosol RObotic NETwork) sun-photometer measurements. Aerosol VC and ER can be reasonably estimated through the application of widely-used polarization lidar, demonstrating a determination coefficient (R²) of 0.89 for VC and 0.77 for ER using the DNN method, as shown in the results. The near-surface height-resolved vertical velocity (VC) and extinction ratio (ER) values from the lidar are consistent with those independently recorded by a collocated Aerodynamic Particle Sizer (APS), as demonstrated. 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 contrast to sun-photometer-derived columnar measurements, this investigation offers a dependable and practical method for determining full-day range-resolved aerosol volume concentration (VC) and extinction ratio (ER) using widespread polarization lidar observations, even in cloudy environments. This research can also be implemented in ongoing, long-term studies using ground-based lidar networks and the CALIPSO space-borne lidar, thus leading to more precise evaluations of aerosol climatic consequences.

Single-photon imaging, with its capability of picosecond resolution and single-photon sensitivity, offers an ideal solution for ultra-long distance imaging in extreme environments. Current single-photon imaging technology is hindered by a slow imaging rate and low-quality images, arising from the impact of quantum shot noise and background noise variations. This research presents a new, efficient single-photon compressed sensing imaging method, which incorporates a uniquely designed mask generated using the Principal Component Analysis and Bit-plane Decomposition techniques. The number of masks is optimized to attain high-quality single-photon compressed sensing imaging under varying average photon counts, while accounting for the effects of quantum shot noise and dark counts on the imaging process. A considerable improvement in both imaging speed and quality has been achieved in comparison to the commonly utilized Hadamard method. BMS309403 With the aid of only 50 masks, the experiment generated a 6464-pixel image, showcasing a 122% sampling compression rate and an 81-fold acceleration in sampling speed.