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. selleck products In terms of polishing efficiency, a 30% increase was noted when measured against manual polishing. The subaperture polishing process stands to benefit from the insightful perspectives offered by the proposed SCP model.
Point defects of differing chemical makeups are concentrated on the surface of most mechanically machined fused silica optical surfaces that have defects, severely impacting their resistance to laser damage under strong laser irradiance. Point defects demonstrate a spectrum of effects on a material's laser damage resistance. Unsurprisingly, the proportions of the different point defects are undefined, thereby hindering a clear understanding of the intrinsic quantitative relationship among them. To gain a complete understanding of the multifaceted impact of various point defects, a thorough investigation of their origins, evolutionary processes, and particularly the quantitative relationships between them is crucial. Following analysis, seven types of point defects have been determined. 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. The photoluminescence (PL) emission spectra and the properties of point defects (such as reaction rules and structural features) further corroborate the conclusions. From the fitted Gaussian components and electronic transition theory, a quantitative connection is constructed for the first time between photoluminescence (PL) and the ratios of different point defects. In terms of representation, E'-Center holds the largest share among the groups. The comprehensive action mechanisms of various point defects are fully revealed by this work, offering novel insights into defect-induced laser damage mechanisms in optical components under intense laser irradiation, viewed from the atomic scale.
Fiber specklegram sensors, in opposition to intricately manufactured and expensive sensing systems, offer a different approach to commonplace fiber sensing technologies. Specklegram demodulation methods, largely reliant on statistical correlations or feature-based classifications, often exhibit restricted measurement ranges and resolutions. We introduce and validate a learning-enhanced, spatially resolved methodology for detecting bending in fiber specklegrams. 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. To validate the proposed method's efficacy and robustness, a series of rigorous experiments were carried out. The results confirm 100% accuracy in predicting the perturbed position, and the average prediction errors for the curvature of the learned and unlearned configurations are 7.791 x 10⁻⁴ m⁻¹ and 7.021 x 10⁻² m⁻¹, respectively. Deep learning is integral to this method, promoting the practical use of fiber specklegram sensors and offering critical insight into the interrogation of sensing signals in the practical context.
High-power mid-infrared (3-5µm) laser propagation through chalcogenide hollow-core anti-resonant fibers (HC-ARFs) shows considerable promise, despite the existing gaps in understanding their properties and the difficulties associated with their fabrication. This paper describes a seven-hole chalcogenide HC-ARF with integrated cladding capillaries, fabricated from purified As40S60 glass, utilizing the combined stack-and-draw method with dual gas path pressure control. In this medium, we predict and empirically validate that higher-order mode suppression, along with multiple low-loss transmission bands, exists within the mid-infrared region. The minimum measured fiber loss at 479µm is a notable 129 dB/m. The fabrication and implication of diverse chalcogenide HC-ARFs are facilitated by our findings, opening avenues for mid-infrared laser delivery systems.
Miniaturized imaging spectrometers encounter obstacles in the process of reconstructing high-resolution spectral images. We introduce, in this study, an optoelectronic hybrid neural network, constructed using a zinc oxide (ZnO) nematic liquid crystal (LC) microlens array (MLA). This architecture optimizes neural network parameters by combining the TV-L1-L2 objective function with the mean square error loss function, maximizing the benefits of ZnO LC MLA. Optical convolution using a ZnO LC-MLA is adopted to decrease the overall size of the network. 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.
Research into the rotational Doppler effect (RDE) is experiencing a surge of interest, extending from acoustic investigations to optical explorations. RDE's observation is primarily contingent upon the probe beam's orbital angular momentum, whereas the perception of radial mode is less clear. For a clearer understanding of radial modes in RDE detection, we explore the interaction mechanism between probe beams and rotating objects using complete Laguerre-Gaussian (LG) modes. The observation of RDE critically hinges upon radial LG modes, demonstrated by both theoretical and experimental approaches, due to the topological spectroscopic orthogonality of the probe beams and objects. The probe beam's performance is improved by employing multiple radial LG modes, enhancing the RDE detection's sensitivity to objects possessing intricate radial structures. Along with this, a particular method of estimating the efficiency of a wide array of probe beams is detailed. selleck products This research has the prospect of innovating RDE detection procedures, leading to related applications being placed on a cutting-edge platform.
This work details the measurement and modeling of tilted x-ray refractive lenses, focusing on their x-ray beam effects. The modeling is evaluated using at-wavelength metrology from x-ray speckle vector tracking (XSVT) experiments conducted at the ESRF-EBS light source's BM05 beamline, resulting in very good concordance. The validation process facilitates our exploration of the potential applications of tilted x-ray lenses within optical design methodologies. We posit that, although tilting 2D lenses appears uninteresting in relation to aberration-free focusing, tilting 1D lenses about their focal direction can be instrumental in facilitating a smooth adjustment of their focal length. Experimental evidence demonstrates a continuous shift in the apparent lens radius of curvature, R, with a reduction exceeding a factor of two, and potential applications in beamline optics are explored.
Assessing aerosol radiative forcing and impacts on climate necessitates understanding microphysical properties like volume concentration (VC) and effective radius (ER). Nevertheless, the spatial resolution of aerosol vertical profiles, VC and ER, remains elusive through remote sensing, barring the integrated columnar measurements achievable with sun-photometers. In this study, a method for retrieving range-resolved aerosol vertical columns (VC) and extinctions (ER) is developed for the first time, using a combination of partial least squares regression (PLSR) and deep neural networks (DNN), while leveraging polarization lidar and simultaneous AERONET (AErosol RObotic NETwork) sun-photometer measurements. The results from employing widely-used polarization lidar indicate that aerosol VC and ER can be reasonably estimated, yielding a determination coefficient (R²) of 0.89 and 0.77 for VC and ER respectively, employing the DNN approach. Supporting evidence from the collocated Aerodynamic Particle Sizer (APS) confirms a strong agreement between the height-resolved vertical velocity (VC) and extinction ratio (ER), as measured by the lidar, in the near-surface region. Significant daily and seasonal fluctuations in atmospheric aerosol VC and ER were observed 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.
For extreme conditions and ultra-long-distance imaging, single-photon imaging technology provides an ideal solution, marked by its picosecond resolution and single-photon sensitivity. Current single-photon imaging technology experiences difficulties with both speed and image quality due to the impact of quantum shot noise and background noise fluctuations. We propose a streamlined single-photon compressed sensing imaging approach within this work, featuring a custom mask derived from the Principal Component Analysis and Bit-plane Decomposition methods. Optimizing the number of masks, considering the effects of quantum shot noise and dark counts on imaging, leads to high-quality single-photon compressed sensing imaging at different average photon counts. A considerable improvement in both imaging speed and quality has been achieved in comparison to the commonly utilized Hadamard method. selleck products 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.