Brillouin Random Lasing Resonance Enabled Fast Light and Superluminal Propagation in Optical Fibers.
Optics express（2025）
Abstract
We proposed and demonstrated fast light and superluminal propagation based on Rayleigh scattering-induced Brillouin random lasing resonance in optical fibers. A theoretical model of the proposed Brillouin superluminal propagation system has been established, detailing the principle of optical group velocity manipulation based on the combination of stimulated Brillouin scattering and Brillouin cross-gain modulation, for the first time to the best of our knowledge. Thanks to the randomly distributed Rayleigh scattering, Brillouin random lasing resonance with a single-longitudinal-mode lasing operation essentially contributes to fast light and superluminal propagation of the pump light signals propagating along a kilometer-long optical fiber. The temporal advancement, group velocity, and group index of sinusoidally modulated pump signals are thoroughly compared in experiments, which agree well with simulations. The dependence of the Brillouin gain fibers on the group velocity as well as the impact of Stokes power under different signal modulation frequencies on the broadening factor are systematically discussed and characterized. Furthermore, the Brillouin random lasing fast-light system is developed to offer a unique time-domain analysis solution for high-sensitivity fiber Bragg grating (FBG) temperature sensing scheme. Scalable sensitivity of the proposed sensing system can be achieved by applying different modulation frequencies. These findings may find wide potential applications in optical signal processing and hyper-sensitive detection.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined