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. 17-AAG in vitro There was a 30% improvement in polishing efficiency, surpassing manual polishing techniques. The proposed SCP model illuminates paths toward progress in the subaperture polishing procedure.

Intense laser irradiation severely degrades the laser damage resistance of mechanically machined fused silica optical surfaces, where the presence of surface defects concentrates point defects of various types. Laser damage resistance is intricately linked to the distinctive contributions of numerous point defects. Specifically, the relative amounts of various point imperfections are unknown, creating a challenge in understanding the fundamental quantitative connection between different point defects. To achieve a complete and comprehensive picture of the effects of different point defects, a systematic study of their origins, rules of development, and especially the quantitative relationship between them is paramount. Seven types of point defects are established within this analysis. The tendency of unbonded electrons within point defects to ionize results in laser damage; a measurable relationship correlates the amounts 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. Based on the Gaussian component fits and electronic transition models, a first-ever quantitative link is derived between photoluminescence (PL) and the quantities of different point defects. Of all the accounts, E'-Center shows the highest percentage. From an atomic perspective, this work significantly contributes to a full understanding of the complex action mechanisms of diverse point defects, providing valuable insights into defect-induced laser damage in optical components under intense laser irradiation.

Instead of complex manufacturing processes and expensive analysis methods, fiber specklegram sensors offer an alternative path in fiber optic sensing technologies, deviating from the standard approaches. Correlation-based specklegram demodulation methods, relying on statistical properties or feature classifications, usually provide limited measurement ranges and resolutions. We introduce and validate a learning-enhanced, spatially resolved methodology for detecting bending in fiber specklegrams. Employing a hybrid framework, this method learns the evolution of speckle patterns. The framework, integrating a data dimension reduction algorithm and a regression neural network, determines curvature and perturbed positions from specklegrams, even for previously unseen curvature configurations. Careful experimentation was conducted to evaluate the proposed scheme's viability and dependability. The results show a prediction accuracy of 100% for the perturbed position, and average prediction errors of 7.791 x 10⁻⁴ m⁻¹ and 7.021 x 10⁻² m⁻¹ were observed for the learned and unlearned curvature configurations, respectively. Utilizing deep learning, this method enhances the practical implementation of fiber specklegram sensors, providing valuable insights into the interrogation of sensing signals.

Chalcogenide hollow-core anti-resonant fibers (HC-ARFs) present an intriguing medium for high-power mid-infrared (3-5µm) laser delivery, but their inherent properties are not fully elucidated and their production remains a substantial hurdle. We detail in this paper a seven-hole chalcogenide HC-ARF with contiguous cladding capillaries, created by combining the stack-and-draw method with a dual gas path pressure control technique using purified As40S60 glass. We hypothesize and experimentally confirm that the medium showcases suppression of higher-order modes and presents multiple low-loss transmission bands in the mid-infrared spectrum. Measurements show losses as low as 129 dB/m at 479 µm. Our research paves the way for the implication and fabrication of diverse chalcogenide HC-ARFs, enabling their use in mid-infrared laser delivery systems.

Miniaturized imaging spectrometers are faced with limitations in the reconstruction of their high-resolution spectral images, stemming from bottlenecks. This research proposes an optoelectronic hybrid neural network architecture utilizing a zinc oxide (ZnO) nematic liquid crystal (LC) microlens array (MLA). By constructing the TV-L1-L2 objective function and employing mean square error as the loss function, this architecture leverages the strengths of ZnO LC MLA to optimize neural network parameters. The ZnO LC-MLA's optical convolution capabilities are harnessed to decrease the network's volume. The architecture's reconstruction of a 1536×1536 pixel hyperspectral image, spanning the wavelengths from 400nm to 700nm, was accomplished in a relatively brief timeframe, and the spectral accuracy of the reconstruction reached a remarkable level of 1nm.

The rotational Doppler effect (RDE) is a subject of significant interest across numerous fields of study, spanning from the realm of acoustics to the field of optics. RDE's detection strongly correlates with the orbital angular momentum of the probe beam; meanwhile, the recognition of radial mode is ambiguous. To illuminate the function of radial modes in RDE detection, we unveil the interaction mechanism between probe beams and rotating objects, employing complete Laguerre-Gaussian (LG) modes. Experimental and theoretical evidence confirms the critical function of radial LG modes in RDE observation, stemming 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. Simultaneously, a distinct approach for evaluating the productivity of varied probe beams is introduced. 17-AAG in vitro The potential exists for this endeavor to transform the approach to RDE detection, leading to the evolution of related applications onto a new operational paradigm.

Measurements and models are used in this study to assess the impact of tilted x-ray refractive lenses on x-ray beams. X-ray speckle vector tracking (XSVT) metrology at the ESRF-EBS light source's BM05 beamline is used to benchmark the modelling; this comparison shows excellent agreement. The validation enables the investigation of potential applications of tilted x-ray lenses in the sphere of optical design. Our findings indicate that the tilting of 2D lenses appears unhelpful for aberration-free focusing, while the tilting of 1D lenses around their focusing axis allows for a seamless and gradual modification of their focal length. Experimental results confirm the ongoing variation in the apparent lens radius of curvature, R, allowing reductions exceeding two times; this opens up potential uses in the design of beamline optics.

Assessing aerosol radiative forcing and impacts on climate necessitates understanding microphysical properties like volume concentration (VC) and effective radius (ER). Unfortunately, the current state of remote sensing technologies prevents the determination of range-resolved aerosol vertical concentration (VC) and extinction (ER), except for the column-integrated measurement from sun-photometer observations. This study introduces, for the first time, a range-resolved aerosol vertical column (VC) and extinction retrieval method, leveraging partial least squares regression (PLSR) and deep neural networks (DNN), and integrating polarization lidar data with concurrent AERONET (AErosol RObotic NETwork) sun-photometer measurements. Analysis of polarization lidar data reveals that the measurement technique can reasonably estimate aerosol VC and ER, producing a determination coefficient (R²) of 0.89 (0.77) for VC (ER) through the implementation of a DNN method. 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). We noted substantial changes in the atmospheric levels of aerosol VC and ER at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL), influenced by daily and seasonal cycles. 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. Furthermore, this investigation is also applicable to ongoing, long-term observations conducted by existing ground-based lidar networks and the space-borne CALIPSO lidar, with the goal of providing a more precise assessment of aerosol climate impacts.

Due to its picosecond resolution and single-photon sensitivity, single-photon imaging technology is the ideal solution for ultra-long-distance imaging under extreme conditions. 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. In this research, we propose a high-efficiency single-photon compressed sensing imaging scheme. A novel mask is developed through the combined application of Principal Component Analysis and Bit-plane Decomposition algorithms. By optimizing the number of masks, high-quality single-photon compressed sensing imaging with different average photon counts is ensured, considering the impact of quantum shot noise and dark count on imaging. The imaging speed and quality have been markedly boosted compared to the frequently implemented Hadamard scheme. 17-AAG in vitro In the experiment, a 6464-pixel image was produced using only 50 masks, leading to a 122% sampling compression rate and an 81-fold increase in sampling speed.