• [1]

    I. M. H. Etherington, Phil. Mag. 15(18), 761 (1933)

  • [2]

    B. A. Bassett and M. Kunz, Phys. Rev. D 69, 101305 (2004)

  • [3]

    P. S. Corasaniti, Mon. Not. Roy. Astron. Soc. 372(1), 191 (2006)

  • [4]

    G. F. R. Ellis, R. Poltis, J.-P. Uzan et al., Phys. Rev. D 87, 103530 (2013)

  • [5]

    R. F. L. Holanda, J. A. S. Lima, and M. B. Ribeiro, Astrophys. J. Lett. 722, L233 (2010)

  • [6]

    Z. Li, P. Wu, and H. W. Yu, Astrophys. J. Lett. 729, L14 (2011)

  • [7]

    N. Liang, Z. Li, P. Wu, et al., Mon. Not. Roy. Astron. Soc. 436(2), 1017 (2013)

  • [8]

    K. Liao, Z. Li, S. Cao et al., Astrophys. J. 822(2), 74 (2016)

  • [9]

    X. Li and H. N. Lin, Mon. Not. Roy. Astron. Soc. 474(1), 313 (2018)

  • [10]

    H.-N. Lin and X. Li, Chin. Phys. C 44(7), 075101 (2020)

  • [11]

    H.-N. Lin, X. Li, and L. Tang, Chin. Phys. C 45(1), 015109 (2021)

  • [12]

    R. Arjona, H.-N. Lin, S. Nesseris et al., Phys. Rev. D 103(10), 103513 (2021)

  • [13]

    F. S. Lima, R. F. L. Holanda, S. H. Pereira et al., JCAP 08, 035 (2021)

  • [14]

    D. M. Scolnic et al., Astrophys. J. 859(2), 101 (2018)

  • [15]

    C. Zhou, J. Hu, M. Li et al., Astrophys. J. 909(2), 118 (2021)

  • [16]

    H.-N. Lin, M.-H. Li, and X. Li, Mon. Not. Roy. Astron. Soc. 480(3), 3117 (2018)

  • [17]

    C.-Z. Ruan, F. Melia, and T.-J. Zhang, Astrophys. J. 866(1), 31 (2018)

  • [18]

    J. Qin, F. Melia, and T.-J. Zhang, Mon. Not. Roy. Astron. Soc. 502(3), 3500 (2021)

  • [19]

    R. F. L. Holanda, V. C. Busti, F. S. Lima et al., JCAP 09, 039 (2017)

  • [20]

    X. Fu and P. Li, Int. J. Mod. Phys. D 26(9), 1750097 (2017)

  • [21]

    L.-X. Li, Mon. Not. Roy. Astron. Soc. 379, L55 (2007)

  • [22]

    H.-N. Lin, X. Li, and Z. Chang, Mon. Not. Roy. Astron. Soc. 455(2), 2131 (2016)

  • [23]

    L. Tang, X. Li, H.-N. Lin et al., Astrophys. J. 907(2), 121 (2021)

  • [24]

    A. Geron, Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems, 2nd Edition, O'Reilly Media, 2017.

  • [25]

    C. Escamilla-Rivera, M. A. C. Quintero, and S. Capozziello, JCAP 03, 008 (2020)

  • [26]

    G.-J. Wang, X.-J. Ma, S.-Y. Li et al., Astrophys. J. Suppl. 246(1), 13 (2020)

  • [27]

    L. Tang, H.-N. Lin, X. Li et al., Mon. Not. Roy. Astron. Soc. 509(1), 1194 (2021)

  • [28]

    T. Liu, S. Cao, S. Zhang et al., Eur. Phys. J. C 81(10), 903 (2021)

  • [29]

    S. Mollerach and E. Roulet, Gravitational lensing and microlensing, World Scientific, Singapore, 2002.

  • [30]

    S. Khedekar and S. Chakraborti, Phys. Rev. Lett. 106, 221301 (2011)

  • [31]

    C. S. Kochanek, Astrophys. J. 384, 1 (1992)

  • [32]

    E. O. Ofek, H.-W. Rix, and D. Maoz, Mon. Not. Roy. Astron. Soc. 343, 639 (2003)

  • [33]

    S. Cao, Y. Pan, M. Biesiada et al., JCAP 03, 016 (2012)

  • [34]

    I. Jorgensen, M. Franx, and P. Kjaergaard, Mon. Not. Roy. Astron. Soc. 276, 1341 (1995)

  • [35]

    M. Cappellari et al., Mon. Not. Roy. Astron. Soc. 366, 1126 (2006)

  • [36]

    Y. Chen, R. Li, Y. Shu et al., Mon. Not. Roy. Astron. Soc. 488(3), 3745 (2019)

  • [37]

    L. V. E. Koopmans, T. Treu, A. S. Bolton et al., Astrophys. J. 649, 599 (2006)

  • [38]

    S. Birrer et al., Mon. Not. Roy. Astron. Soc. 484, 4726 (2019)

  • [39]

    B. Wang, J.-Z. Qi, J.-F. Zhang et al., Astrophys. J. 898(2), 100 (2020)

  • [40]

    S. Räsänen, K. Bolejko, and A. Finoguenov, Phys. Rev. Lett. 115(10), 101301 (2015)

  • [41]

    B. Karlik, A. V. Olgac, Performance analysis of various activation functions in generalized mlp architectures of neural networks, International Journal of Artificial Intelligence and Expert Systems, 1(4), 111 (2011). http://www.cscjournals.org/library/manuscriptinfo.php

  • [42]

    A. F. Agarap, Deep Learning using Rectified Linear Units (ReLU), arxiv: 1803.08375

  • [43]

    D.-A. Clevert, T. Unterthiner, and S. Hochreiter, Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs), arXiv: 1511.07289

  • [44]

    G. Klambauer, T. Unterthiner, A. Mayr et al., Self-Normalizing Neural Networks, arXiv: 1706.02515

  • [45]

    Y. Gal and Z. Ghahramani, A Theoretically Grounded Application of Dropout in Recurrent Neural Networks, arXiv: 1512.05287

  • [46]

    Y. Gal and Z. Ghahramani, Dropout as a Bayesian Approximation: Appendix, arXiv: 1506.02157

  • [47]

    Y. Gal and Z. Ghahramani, Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning, arXiv: 1506.02142

  • [48]

    D. Muthukrishna, G. Narayan, K. S. Mandel et al., Publ. Astron. Soc. Pac. 131(1005), 118002 (2019)

  • [49]

    V. Bonjean, Astron. Astrophys. 634, A81 (2020)

  • [50]

    T. Mangena, S. Hassan, and M. G. Santos, Mon. Not. Roy. Astron. Soc. 494(1), 600 (2020)

  • [51]

    D. Foreman-Mackey, D. W. Hogg, D. Lang et al., Publ. Astron. Soc. Pac. 125, 306 (2013)

  • [52]

    S. Cao, M. Biesiada, M. Yao et al., Mon. Not. Roy. Astron. Soc. 461(2), 2192 (2016)