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模式能量梯度上升的光束入腔指向方法

邢成文,  徐天尧,  马超群,  栾苏琪,  李跃,  孟范超,  孟令强,  吕刚,  印雄飞,  贾建军

邢成文, 徐天尧, 马超群, 等. 模式能量梯度上升的光束入腔指向方法[J]. 中国光学(中英文), 2025, 18(3): 682-690. doi: 10.37188/CO.2025-0053
引用本文: 邢成文, 徐天尧, 马超群, 等. 模式能量梯度上升的光束入腔指向方法[J]. 中国光学(中英文), 2025, 18(3): 682-690. doi: 10.37188/CO.2025-0053
XING Cheng-wen, XU Tian-yao, MA Chao-qun, et al. Cavity alignment method based on mode energy gradient ascent[J]. Chinese Optics, 2025, 18(3): 682-690. doi: 10.37188/CO.2025-0053
Citation: XING Cheng-wen, XU Tian-yao, MA Chao-qun, et al. Cavity alignment method based on mode energy gradient ascent[J]. Chinese Optics, 2025, 18(3): 682-690. doi: 10.37188/CO.2025-0053

模式能量梯度上升的光束入腔指向方法

cstr: 32171.14.CO.2025-0053
基金项目: 国家重点研发计划(No. 2024YFC2206900,No. 2021YFC2201804)
详细信息
    作者简介:

    邢成文(1996—),男,安徽阜阳人,博士研究生,主要研究领域为激光技术。E-mail:xingchengwen21@mails.ucas.ac.cn

    印雄飞(1973—),男,江苏泰兴人,博士,教授,2000年于上海交通大学获得博士学位。主要研究方向为激光精密测量与加工。E-mail:yinxiongfei@ucas.ac.cn

    贾建军(1972—),男,山西永济人,博士,研究员,1999年于上海交通大学获得博士学位,主要研究领域为空间光电技术。E-mail:jjjun10@mail.sitp.ac.cn

  • 中图分类号: O43

Cavity alignment method based on mode energy gradient ascent

Funds: Supported by National Key R & D Program of China (No. 2024YFC2206900, No. 2021YFC2201804)
More Information
  • 摘要:

    为了解决光束与法布里珀罗腔的入腔失调问题,本文基于谐振模式能量梯度上升自适应调整双反射镜步进,实现所需谐振模式的入腔光束指向。首先,利用双反射镜步进与入腔光束失调的关系,提出分离式入腔光束平移与角度调整方法。其次,利用EfficientNET神经网络对谐振模式图片进行分类,实现不同激光模式的图像识别。最后,利用腔后模式能量梯度调整双反射镜步进,低成本、高效率实现目标谐振模式的入腔耦合。本文的入腔光束指向调整方法为超稳激光器以及引力波探测中法布里珀罗腔的入腔耦合提供了新思路。

     

  • 图 1  光束入腔失调引起的模式能量分布

    Figure 1.  Mode energy distribution induced by beam misalignment

    图 2  不同阶模式的对数能量与失调量的关系

    Figure 2.  Relationship between logarithmic energy of optical modes and misalignment

    图 3  梯度上升法求解第n阶模式能量的极值点

    Figure 3.  Finding the maximum of n-th order mode energy utilizing gradient ascent method

    图 4  双反射镜系统角度组合方案。 (a)平移; (b)旋转

    Figure 4.  Angle combination strategies for dual-mirror systems. (a) Lateral offset; (b) tilt

    图 5  谐振模式数据采集装置

    Figure 5.  Resonant mode data acquisition device

    图 6  谐振模式数据集示例

    Figure 6.  Examples of the resonant mode dataset

    图 7  利用EfficientNet在数据集上训练的准确度和损失曲线

    Figure 7.  Accuracy and loss curves of training on the dataset using EfficientNet

    图 8  利用EfficientNet在测试集上模式图片识别的混淆矩阵

    Figure 8.  Confusion matrix of the mode picture recognition on the test dataset by EfficientNet

    图 9  不同模式的强度图。(a) HG00模式;(b) HG01模式;(c) HG02模式;(d) LG01模式

    Figure 9.  Intensity maps under different modes. (a) HG00; (b) HG01; (c) HG02; (d) LG01

    图 10  实验装置框图

    Figure 10.  Block diagram of the experimental set-up

    图 11  实验装置图

    Figure 11.  Experimental set-up

    图 12  基模强度对数值与调整角度的关系

    Figure 12.  Relationship between the tilt angles and logarithmic intensity of the fundamental mode

    图 13  基于梯度上升的入腔调整结果

    Figure 13.  Cavity recoupling based on mode energy gradient ascent

    图 14  锁定激光到不同模式上

    Figure 14.  Laser frequency locking on different optical modes

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出版历程
  • 收稿日期:  2024-03-15
  • 修回日期:  2024-04-14
  • 网络出版日期:  2025-04-29

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