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The speckle field yielded by disordered media is extensively employed for spectral measurements. Existing speckle reconstructive spectrometers (RSs) implemented by neural networks primarily rely on supervised learning, which necessitates large-scale spectra-speckle pairs. However, beyond system stability requirements for prolonged data collection, generating diverse spectra with high resolution and finely labeling them is particularly difficult. A lack of variety in datasets hinders the generalization of neural networks to new spectrum types. Here we avoid this limitation by introducing PhyspeNet, an untrained spectrum reconstruction framework combining a convolutional neural network (CNN) with a physical model of a chaotic optical cavity. Without pre-training and prior knowledge about the spectrum under test, PhyspeNet requires only a single captured speckle for various multi-wavelength reconstruction tasks. Experimentally, we demonstrate a lens-free, snapshot RS system by leveraging the one-to-many mapping between spatial and spectrum domains in a random medium. Dual-wavelength peaks separated by 2 pm can be distinguished, and a maximum working bandwidth of 40 nm is achieved with high measurement accuracy. This approach establishes a new paradigm for neural network-based RS systems, entirely eliminating reliance on datasets while ensuring that computational results exhibit a high degree of generalizability and physical explainability., Comment: 12 pages, 4 figures

Since the advent of optical orbital angular momentum (OAM), advances in the generation and manipulation of OAM beams have continuously impacted on intriguing applications including optical communication, optical tweezers, and remote sensing. To realize the generation of high-power and fast switchable OAM beams, coherent combining of fiber lasers offers a promising way. Here in this contribution, we comprehensively investigate the coherent fiber laser array system for structuring OAM beams in terms of the design considerations and performance analysis. The performance metric and evaluation method of the laser array system are presented and introduced. Accordingly, the effect of the main sections of the laser array system, namely the high-power laser sources, emitting array configuration, and dynamic control system, on the performance of the output coherently combined OAM beams is evaluated, which reveals the system tolerance of perturbative factors and provides the guidance on system design and optimization. This work could provide beneficial reference on the practical implementation of spatially structuring high-power, fast switchable OAM beams with fiber laser arrays.

Coherent beam combination (CBC) of fiber lasers holds promise for achieving high brightness laser systems, which have given rise to widespread applications such as particle accelerator, space debris removal, and industrial fabrication. The emitting laser array of CBC systems offers intriguing features in terms of agile beam steering, flexible beam shaping, and high scalability for output power and array elements. However, the theoretical model of the laser array in CBC systems is less well explored beyond the routine angular-spectrum method, where methods for optimizing the laser array configuration are more limited. Here, we explore the theory for the laser array of CBC systems in the view of angular domain. The laser array is represented by the composition of angular harmonics, the orthogonal basis over the azimuthal plane, and we elucidate the formation of mainlobe and sidelobes of the far-field interference pattern by using the orbital angular momentum spectrum analysis and azimuthal decomposition. Based on our findings, a fractal-based laser array configuration is proposed to enhance the performance of the combining system. Our work offers a deeper insight into the theoretical study and application of laser beam combination and opens opportunities for the further optimization of CBC implementations.

Raman fiber lasers (RFLs) are currently promising and versatile light sources for a variety of applications. So far, operations of high power and brightness-enhanced RFLs have absorbed enormous interests along with rapid progress. Nevertheless, the stable Raman lasing at high power levels remains challenged by the thermal effects. In an effort to realize more effective thermal management in high power RFLs, here we demonstrate, for the first time, an all-fiberized RFA employing metal-coated passive fiber enabling high power lasing. By employing aluminum to the cladding of graded-index (GRIN) passive fiber, the thermal abstraction of the laser devices is more sufficient to support low-temperature operation. The maximum output power reaches 3.083 kW at 1130 nm with a conversion efficiency of 78.7%. To the best of our knowledge, this is the first Raman laser generation based on metal-coated passive fiber. Meanwhile, it is also the highest power attained in the fields of all kinds of Raman lasers based on merely nonlinear gain., Comment: 10 pages, 5 figures

In this work, we propose a unified spatio-temporal model to study the temporal dynamics in continuously pumped ytterbium-doped fiber lasers (YDFLs). Different from previously reported theories, this model is capable of obtaining the temporal evolution of an YDFL from relaxation oscillation region to relative stable region in different time scales ranging from sub-nanosecond to millisecond. It reveals that there exists dual time scale characteristics in the temporal evolution of a multi-longitudinal mode YDFL. Specifically, the temporal evolution would experience sharp change during one cavity round-trip while keep relatively stable between adjacent cavity round-trips. Representative cases are simulated to study the influences of structure parameters on the temporal dynamics and the longitudinal mode characteristics in YDFLs. Three types of temporal instabilities, i.e. sustained self-pulsing, self-mode locking, and turbulence-like pulsing, coexist in a multi-longitudinal mode YDFL. The simulation results clarify that the three temporal instabilities are all the reflectors of intrinsic characteristics of longitudinal modes superposition in multi-longitudinal mode YDFLs. In addition, the strength of the irregular sustained self-pulsing is the major issue which impacts the macroscopic temporal fluctuations in YDFLs., Comment: 13 pages, 13 figures

In this work, we derive and present the coupled amplitude equations to describe the evolutions of different spectral components in different transverse modes for Raman fiber amplifiers (RFAs). Both the effects of the four-wave-mixing in the fundamental mode (FM FWM) and the inter-modal four-wave-mixing (IM FWM) on high-power RFAs are demonstrated through numerical simulations. Specifically, effective FM FWM interactions could occur and lead to a drop of the Raman threshold for RFAs by over 50%, despite that the corresponding wave-vector mismatch is rather big. In addition, the IM FWM could also impact the Raman threshold for RFAs with additional generation of the first order Raman Stokes light in the higher-order mode. We also investigate the effects of the intensity fluctuations in the initial inserted pump and seed lasers on high-power RFAs. It reveals that the temporal stability of the initial inserted pump laser have much more significant impacts on high-power RFAs than that of the initial inserted seed laser. Notably, through applying temporal stable laser as the initial inserted pump laser, both the FM FWM and IM FWM effects could be effectively suppressed, and the Raman threshold for high-power RFAs could be increased by over twice., Comment: 12 pages, 9 figures

By focusing on a typical emitting wavelength of 1120 nm as an example, we present the first demonstration of a high-efficiency, narrow-linewidth kilowatt-level all-fiber amplifier based on hybrid ytterbium-Raman (Yb-Raman) gains. Notably, two temporally stable, phase-modulated single-frequency lasers operating at 1064 nm and 1120nm, respectively, were applied in the fiber amplifier, to alleviate the spectral broadening of the 1120 signal laser and suppress the stimulated Brillouin scattering (SBS) effect simultaneously. Over 1 kW narrow-linewidth 1120 nm signal laser was obtained with a slope efficiency of ~ 77% and a beam quality of M2~1.21. The amplified spontaneous emission (ASE) noise in the fiber amplifier was effectively suppressed by incorporating an ASE-filtering system between the seed laser and the main amplifier. Further examination of the influence of power ratios between the two seed lasers on the conversion efficiency had proved that the presented amplifier could work efficiently when the power ratio of 1120 nm seed laser ranged from 31% to 61%. Overall, this setup could provide a well reference for obtaining high power narrow-linewidth fiber lasers operating within 1100-1200 nm., Comment: 7 pages, 6 figures

The Hermite-Gaussian (HG) modes make up a complete and orthonormal basis, which have been extensively used to describe optical fields. Here, we demonstrate, for the first time to our knowledge, deep learning-based modal decomposition (MD) of HG beams. This method offers a fast, economical and robust way to acquire both the power content and phase information through a single-shot beam intensity image, which will be beneficial for the beam shaping, beam quality assessment, studies of resonator perturbations, and other further research on the HG beams., Comment: 6 figures

We introduce deep learning technique to predict the beam propagation factor M^2 of the laser beams emitting from few-mode fiber for the first time, to the best of our knowledge. The deep convolutional neural network (CNN) is trained with paired data of simulated near-field beam patterns and their calculated M^2 value, aiming at learning a fast and accurate mapping from the former to the latter. The trained deep CNN can then be utilized to evaluate M^2 of the fiber beams from single beam patterns. The results of simulated testing samples have shown that our scheme can achieve an averaged prediction error smaller than 2% even when up to 10 eigenmodes are involved in the fiber. The error becomes slightly larger when heavy noises are added into the input beam patterns but still smaller than 2.5%, which further proves the accuracy and robustness of our method. Furthermore, the M^2 estimation takes only about 5 ms for a prepared beam pattern with one forward pass, which can be adopted for real-time M^2 determination with only one supporting Charge-Coupled Device (CCD). The experimental results further prove the feasibility of our scheme. Moreover, the method we proposed can be confidently extended to other kinds of beams provided that adequate training samples are accessible. Deep learning paves the way to superfast and accurate M^2 evaluation with very low experimental efforts., Comment: 12 pages, 10 figures

We incorporate deep learning (DL) into coherent beam combining (CBC) systems for the first time, to the best of our knowledge. Using a well-trained convolutional neural network DL model, the phase error in CBC systems could be accurately estimated and preliminarily compensated. Then, the residual phase error is further compensated by stochastic parallel gradient descent (SPGD) algorithms. The two-stage phase control strategy combined with DL and SPGD algorithms is validated to be a feasible and promising technique to alleviate the long-standing problem that the phase control bandwidth decreases as the number of array elements expands. Further investigation denotes that the proposed phase control technique could be employed to generate orbital angular momentum (OAM) beams with different orders by distinguishing the OAM beams of conjugated phase distributions.