[1] B. P. Abbott, R. Abbott, T. D. Abbott et al. Observation of gravitational waves from a binary black hole merger. Phys. Rev. Lett., 116, 061102(2016).

[2] W. M. Lee, K. Dholakia. Optically trapped and controlled microapertures for studies of spatial coherence in an arbitrary light field. Appl. Phys. Lett., 90, 261101(2007).

[3] C. H. Gan, G. Gbur, T. D. Visser. Surface plasmons modulate the spatial coherence of light in Young’s interference experiment. Phys. Rev. Lett., 98, 043908(2007).

[4] V. Hilbert, C. Rödel, G. Brenner et al. Spatio-temporal coherence of free-electron laser radiation in the extreme ultraviolet determined by a Michelson interferometer. Appl. Phys. Lett., 105, 101102(2014).

[5] P. Lu, L. Q. Men, K. Sooley et al. Tapered fiber Mach–Zehnder interferometer for simultaneous measurement of refractive index and temperature. Appl. Phys. Lett., 94, 131110(2009).

[6] D. G. Matei, T. Legero, S. Häfner et al. 1.5 µm Lasers with sub-10 mHz linewidth. Phys. Rev. Lett., 118, 263202(2017).

[7] Y. H. Lai, M. G. Suh, Y. K. Lu et al. Earth rotation measured by a chip-scale ring laser gyroscope. Nat. Photonics, 14, 345(2020).

[8] R. Silvestri, H. Yu, T. Strömberg et al. Experimental observation of Earth’s rotation with quantum entanglement. Sci. Adv., 10, eado0215(2024).

[9] B. Khajavi, E. J. Galvez. Determining topological charge of an optical beam using a wedged optical flat. Opt. Lett., 42, 1516(2017).

[10] W. D. Astuti, H. Matsukuma, M. Nakao et al. An optical frequency domain angle measurement method based on second harmonic generation. Sensors, 21, 670(2021).

[11] W. J. Wu, S. Z. Chen, W. H. Xu et al. Weak-value amplification for the optical signature of topological phase transitions. Photon. Res., 8, B47(2020).

[12] P. B. Dixon, D. J. Starling, A. N. Jordan et al. Ultrasensitive beam deflection measurement via interferometric weak value amplification. Phys. Rev. Lett., 102, 173601(2009).

[13] Z. Yu, K. Peng, X. Liu et al. A high-precision absolute angular-displacement capacitive sensor using three-stage time-grating in conjunction with a remodulation scheme. IEEE Trans. Ind. Electron., 66, 7376(2019).

[14] X. Dai, O. Sasaki, J. E. Greivenkamp et al. High accuracy, wide range, rotation angle measurement by the use of two parallel interference patterns. Appl. Opt., 36, 6190(1997).

[15] K. Shi, J. Su, W. Hou. Roll angle measurement system based on differential plane mirror interferometer. Opt. Express, 26, 19826(2018).

[16] B. J. Eves, I. D. Leroux, A. J. Cen. Phase shifting angle interferometer. Metrologia, 60, 055006(2023).

[17] Y. LeCun, Y. Bengio, G. Hinton. Deep learning. Nature, 521, 436(2015).

[18] H. Kurt, M. Kayfeci. Prediction of thermal conductivity of ethylene glycol–water solutions by using artificial neural networks. Appl. Energy, 86, 2244(2009).

[19] M. A. Hearst, S. T. Dumais, E. Osuna et al. Support vector machines. IEEE Intell. Syst. Appl., 13, 18(1998).

[20] J. R. Quinlan. Induction of decision trees. Mach. Learn., 1, 81(1986).

[21] K. He, X. Zhang, S. Ren et al. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770(2016).

[22] B. L. Li, H. T. Luan, K. Y. Li et al. Orbital angular momentum optical communications enhanced by artificial intelligence. J. Opt., 24, 094003(2022).

[23] J. Gray, L. Banchi, A. Bayat et al. Machine-learning-assisted many-body entanglement measurement. Phys. Rev. Lett., 121, 150503(2018).

[24] R. Li, Z. Liu, A. Rohskopf et al. A deep neural network interatomic potential for studying thermal conductivity of β-Ga2O3. Appl. Phys. Lett., 117, 152102(2020).

[25] W. Jeon, W. Jeong, K. Son et al. Speckle noise reduction for digital holographic images using multi-scale convolutional neural networks. Opt. Lett., 43, 4240(2018).

[26] Z. Q. Li, X. Y. Li, R. G. Liang. Random two-frame interferometry based on deep learning. Opt. Express, 28, 24747(2020).

[27] K. Q. Wang, Y. Li, Q. Kemao et al. One-step robust deep learning phase unwrapping. Opt. Express, 27, 15100(2019).

[28] J. W. Zhou, J. H. Tang, Y. L. Yin et al. Fundamental probing limit on the high-order orbital angular momentum of light. Opt. Express, 32, 5339(2024).

[29] M. Cao, Y. L. Yin, J. W. Zhou et al. Machine learning based accurate recognition of fractional optical vortex modes in atmospheric environment. Appl. Phys. Lett., 119, 141103(2021).

[30] J. W. Zhou, Y. Y. Yin, J. H. Tang et al. Recognition of high-resolution optical vortex modes with deep residual learning. Phys. Rev. A, 106, 013519(2022).

[31] M. V. Berry. Optical vortices evolving from helicoidal integer and fractional phase steps. J. Opt. A, 6, 259(2004).

[32] O. Russakovsky, J. Deng, H. Su et al. ImageNet large scale visual recognition challenge. Int. J. Comput. Vis., 115, 211(2014).

[33] C. Szegedy, W. Liu, Y. Q. Jia et al. Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1(2015).

[34] S. Xie, R. B. Girshick, P. Dollár et al. Aggregated residual transformations for deep neural networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5987(2017).

[35] A. Krizhevsky, I. Sutskever, G. E. Hinton. ImageNet classification with deep convolutional neural networks. Commun. ACM, 60, 84(2017).

[36] D. P. Kingma, J. J. C. Ba. Adam: a method for stochastic optimization(2014).