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Free Access
Issue
A&A
Volume 636, April 2020
Article Number A94
Number of page(s) 12
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/201937014
Published online 24 April 2020
  1. Abadi, M., Agarwal, A., Barham, P., et al. 2015, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, Software Available from tensorflow.org [Google Scholar]
  2. Akiyama, K., Lu, R.-S., Fish, V. L., et al. 2015, ApJ, 807, 150 [NASA ADS] [CrossRef] [Google Scholar]
  3. Ball, N. M., Brunner, R. J., Myers, A. D., & Tcheng, D. 2006, ApJ, 650, 497 [NASA ADS] [CrossRef] [Google Scholar]
  4. Bellinger, E. P., Angelou, G. C., Hekker, S., et al. 2016, ApJ, 830, 31 [NASA ADS] [CrossRef] [Google Scholar]
  5. Bird, S., Harris, W. E., Blakeslee, J. P., & Flynn, C. 2010, A&A, 524, A71 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  6. Bisnovatyi-Kogan, G. S., & Ruzmaikin, A. A. 1976, Ap&SS, 42, 401 [NASA ADS] [CrossRef] [Google Scholar]
  7. Bower, G. C., Goss, W. M., Falcke, H., Backer, D. C., & Lithwick, Y. 2006, ApJ, 648, L127 [NASA ADS] [CrossRef] [Google Scholar]
  8. Broderick, A. E., Gold, R., Karami, M., et al. 2020, ApJ, submitted [Google Scholar]
  9. Bronzwaer, T., Davelaar, J., Younsi, Z., et al. 2018, A&A, 613, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  10. Cantiello, M., Blakeslee, J. P., Ferrarese, L., et al. 2018, ApJ, 856, 126 [NASA ADS] [CrossRef] [Google Scholar]
  11. Chael, A., Bouman, K., Johnson, M., Blackburn, L., & Shiokawa, H. 2018, https://doi.org/10.5281/zenodo.1173414 [Google Scholar]
  12. Chael, A. A., Bouman, K. L., Johnson, M. D., et al. 2019a, Astrophysics Source Code Library [record ascl:1904.004] [Google Scholar]
  13. Chael, A., Narayan, R., & Johnson, M. D. 2019b, MNRAS, 486, 2873 [NASA ADS] [CrossRef] [Google Scholar]
  14. Chandra, M., Gammie, C. F., Foucart, F., & Quataert, E. 2015, ApJ, 810, 162 [NASA ADS] [CrossRef] [Google Scholar]
  15. Chollet, F. 2015, Keras, https://keras.io [Google Scholar]
  16. Davelaar, J., Mościbrodzka, M., Bronzwaer, T., & Falcke, H. 2018, A&A, 612, A34 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  17. Davelaar, J., Olivares, H., Porth, O., et al. 2019, A&A, 632, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  18. Dexter, J., McKinney, J. C., & Agol, E. 2012, MNRAS, 421, 1517 [NASA ADS] [CrossRef] [Google Scholar]
  19. Event Horizon Telescope Collaboration (Akiyama, K., et al.) 2019a, ApJ, 875, L1 [NASA ADS] [CrossRef] [Google Scholar]
  20. Event Horizon Telescope Collaboration (Akiyama, K., et al.) 2019b, ApJ, 875, L2 [NASA ADS] [CrossRef] [Google Scholar]
  21. Event Horizon Telescope Collaboration (Akiyama, K., et al.) 2019c, ApJ, 875, L3 [NASA ADS] [CrossRef] [Google Scholar]
  22. Event Horizon Telescope Collaboration (Akiyama, K., et al.) 2019d, ApJ, 875, L4 [NASA ADS] [CrossRef] [Google Scholar]
  23. Event Horizon Telescope Collaboration (Akiyama, K., et al.) 2019e, ApJ, 875, L5 [NASA ADS] [CrossRef] [Google Scholar]
  24. Event Horizon Telescope Collaboration (Akiyama, K., et al.) 2019f, ApJ, 875, L6 [NASA ADS] [CrossRef] [Google Scholar]
  25. Fadely, R., Hogg, D. W., & Willman, B. 2012, ApJ, 760, 15 [NASA ADS] [CrossRef] [Google Scholar]
  26. Falcke, H., Melia, F., & Agol, E. 2000, ApJ, 528, L13 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  27. Fan, X., Li, J., Li, X., Zhong, Y., & Cao, J. 2019, Sci. China Phys. Mech. Astron., 62, 969512 [CrossRef] [Google Scholar]
  28. Fish, V. L., Shea, M., & Akiyama, K. 2020, Adv. Space Res., 65, 821 [NASA ADS] [CrossRef] [Google Scholar]
  29. Fishbone, L. G., & Moncrief, V. 1976, ApJ, 207, 962 [NASA ADS] [CrossRef] [Google Scholar]
  30. Fromm, C. M., Younsi, Z., Baczko, A., et al. 2019, A&A, 629, A4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  31. Gal, Y. 2016, PhD Thesis, University of Cambridge [Google Scholar]
  32. Gal, Y., & Ghahramani, Z. 2015a, ArXiv e-prints [arXiv:1506.02142] [Google Scholar]
  33. Gal, Y., & Ghahramani, Z. 2015b, ArXiv e-prints [arXiv:1506.02158] [Google Scholar]
  34. Gebhardt, K., Adams, J., Richstone, D., et al. 2011, ApJ, 729, 119 [NASA ADS] [CrossRef] [Google Scholar]
  35. George, D., & Huerta, E. A. 2018, Phys. Lett. B, 778, 64 [NASA ADS] [CrossRef] [Google Scholar]
  36. Goddi, C., Falcke, H., Kramer, M., et al. 2017, Int. J. Mod. Phys. D, 26, 1730001 [NASA ADS] [CrossRef] [Google Scholar]
  37. Goodman, J., & Narayan, R. 1989, MNRAS, 238, 995 [NASA ADS] [CrossRef] [Google Scholar]
  38. Gralla, S. E., Holz, D. E., & Wald, R. M. 2019, Phys. Rev. D, 100, 024018 [NASA ADS] [CrossRef] [Google Scholar]
  39. Hastie, T., Tibshirani, R., & Friedman, J. 2001, The Elements of Statistical Learning, Springer Series in Statistics (New York, NY, USA: Springer New York Inc.) [CrossRef] [MathSciNet] [Google Scholar]
  40. He, K., Zhang, X., Ren, S., & Sun, J. 2015, ArXiv e-prints [arXiv:1512.03385] [Google Scholar]
  41. Hendriks, L., & Aerts, C. 2019, PASP, 131, 108001 [NASA ADS] [CrossRef] [Google Scholar]
  42. Hezaveh, Y. D., Perreault Levasseur, L., & Marshall, P. J. 2017, Nature, 548, 555 [NASA ADS] [CrossRef] [Google Scholar]
  43. Hon, M., Stello, D., & Yu, J. 2017, MNRAS, 469, 4578 [NASA ADS] [CrossRef] [Google Scholar]
  44. Howes, G. G. 2010, MNRAS, 409, L104 [NASA ADS] [Google Scholar]
  45. Hunter, J. D. 2007, Comput. Sci. Eng., 9, 90 [Google Scholar]
  46. Jacobs, C., Glazebrook, K., Collett, T., More, A., & McCarthy, C. 2017, MNRAS, 471, 167 [NASA ADS] [CrossRef] [Google Scholar]
  47. Johannsen, T., & Psaltis, D. 2010, ApJ, 718, 446 [NASA ADS] [CrossRef] [Google Scholar]
  48. Johnson, M. D., & Gwinn, C. R. 2015, ApJ, 805, 180 [NASA ADS] [CrossRef] [Google Scholar]
  49. Johnson, M. D., Lupsasca, A., Strominger, A., et al. 2020, Sci. Adv., 6, eaaz1310 [CrossRef] [Google Scholar]
  50. Jones, E., Oliphant, T., Peterson, P., et al. 2001, SciPy: Open Source Scientific Tools for Python [Online] [Google Scholar]
  51. Kendall, A., & Gal, Y. 2017, in Advances in Neural Information Processing Systems 30, eds. I. Guyon, U. V. Luxburg, S. Bengio, et al. (Curran Associates, Inc.), 5574 [Google Scholar]
  52. Kerr, R. P. 1963, Phys. Rev. Lett., 11, 237 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  53. Kim, E. J., & Brunner, R. J. 2017, MNRAS, 464, 4463 [NASA ADS] [CrossRef] [Google Scholar]
  54. Kim, E. J., Brunner, R. J., & Carrasco Kind, M. 2015, MNRAS, 453, 507 [NASA ADS] [CrossRef] [Google Scholar]
  55. Kingma, D. P., & Ba, J. 2014, ArXiv e-prints [arXiv:1412.6980] [Google Scholar]
  56. Kiureghian, A. D., & Ditlevsen, O. 2009, Struct. Saf., 31, 105 [CrossRef] [Google Scholar]
  57. Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2012, in Advances in Neural Information Processing Systems 25, eds. F. Pereira, C. J. C. Burges, L. Bottou, & K. Q. Weinberger (Curran Associates, Inc.), 1097 [Google Scholar]
  58. Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. 1998, Proc. IEEE, 2278 [CrossRef] [Google Scholar]
  59. Lukic, V., & Brüggen, M. 2017, in Astroinformatics, eds. M. Brescia, S. G. Djorgovski, E. D. Feigelson, G. Longo, & S. Cavuoti, IAU Symp., 325, 217 [NASA ADS] [Google Scholar]
  60. MacKay, D. J. C. 1992, Neural Comput., 4, 448 [CrossRef] [Google Scholar]
  61. Millman, K. J., & Aivazis, M. 2011, Comput. Sci. Eng., 13, 9 [CrossRef] [Google Scholar]
  62. Mizuno, Y., Younsi, Z., Fromm, C. M., et al. 2018, Nat. Astron., 2, 585 [NASA ADS] [CrossRef] [Google Scholar]
  63. Mościbrodzka, M., Falcke, H., & Shiokawa, H. 2016, A&A, 586, A38 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  64. Mościbrodzka, M., Dexter, J., Davelaar, J., & Falcke, H. 2017, MNRAS, 468, 2214 [NASA ADS] [CrossRef] [Google Scholar]
  65. Nair, V., & Hinton, G. E. 2010, in Rectified Linear Units Improve Restricted Boltzmann Machines, eds. J. Fürnkranz, & T. Joachims (Omnipress), 807 [Google Scholar]
  66. Narayan, R., & Goodman, J. 1989, MNRAS, 238, 963 [NASA ADS] [Google Scholar]
  67. Narayan, R., Igumenshchev, I. V., & Abramowicz, M. A. 2003, PASJ, 55, L69 [NASA ADS] [Google Scholar]
  68. Narayan, R., SÄdowski, A., Penna, R. F., & Kulkarni, A. K. 2012, MNRAS, 426, 3241 [NASA ADS] [CrossRef] [Google Scholar]
  69. Narayan, R., Johnson, M. D., & Gammie, C. F. 2019, ApJ, 885, L33 [NASA ADS] [CrossRef] [Google Scholar]
  70. Odewahn, S. C., Stockwell, E. B., Pennington, R. L., Humphreys, R. M., & Zumach, W. A. 1992, in Digitised Optical Sky Surveys, eds. H. T. MacGillivray, & E. B. Thomson, Astrophys. Space Sci. Lib., 174, 215 [NASA ADS] [CrossRef] [Google Scholar]
  71. Oliphant, T. E. 2007, Comput. Sci. Eng., 9, 10 [CrossRef] [PubMed] [Google Scholar]
  72. Olivares, H., Porth, O., Davelaar, J., et al. 2019, A&A, 629, A61 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  73. Palumbo, D., Johnson, M., Doeleman, S., Chael, A., & Bouman, K. 2018, Am. Astron. Soc. Meet. Abstr., 231, 347.21 [NASA ADS] [Google Scholar]
  74. Perreault Levasseur, L., Hezaveh, Y. D., & Wechsler, R. H. 2017, ApJ, 850, L7 [NASA ADS] [CrossRef] [Google Scholar]
  75. Petrillo, C. E., Tortora, C., Chatterjee, S., et al. 2017, MNRAS, 472, 1129 [NASA ADS] [CrossRef] [Google Scholar]
  76. Porth, O., Olivares, H., Mizuno, Y., et al. 2017, Comput. Astrophys. Cosmol., 4, 1 [NASA ADS] [CrossRef] [Google Scholar]
  77. Porth, O., Chatterjee, K., Narayan, R., et al. 2019, ApJS, 243, 26 [NASA ADS] [CrossRef] [Google Scholar]
  78. Psaltis, D., Özel, F., Chan, C.-K., & Marrone, D. P. 2015, ApJ, 814, 115 [NASA ADS] [CrossRef] [Google Scholar]
  79. Ressler, S. M., Tchekhovskoy, A., Quataert, E., Chandra, M., & Gammie, C. F. 2015, MNRAS, 454, 1848 [NASA ADS] [CrossRef] [Google Scholar]
  80. Roelofs, F., Falcke, H., Brinkerink, C., et al. 2019, A&A, 625, A124 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  81. Rowan, M. E., Sironi, L., & Narayan, R. 2017, ApJ, 850, 29 [NASA ADS] [CrossRef] [Google Scholar]
  82. Ryan, B. R., Ressler, S. M., Dolence, J. C., Gammie, C., & Quataert, E. 2018, ApJ, 864, 126 [NASA ADS] [CrossRef] [Google Scholar]
  83. Schwarzschild, K. 1916, Sitzungsberichte der Königlich Preussischen Akademie der Wissenschaften zu Berlin, Phys.-Math. Klasse, 189 [Google Scholar]
  84. Sevilla-Noarbe, I., & Etayo-Sotos, P. 2015, Astron. Comput., 11, 64 [NASA ADS] [CrossRef] [Google Scholar]
  85. Shen, H., Huerta, E. A., & Zhao, Z. 2019, ArXiv e-prints [arXiv:1903.01998] [Google Scholar]
  86. Simonyan, K., & Zisserman, A. 2014, ArXiv e-prints [arXiv:1409.1556] [Google Scholar]
  87. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. 2014, J. Mach. Learn. Res., 15, 1929 [Google Scholar]
  88. Suchkov, A. A., Hanisch, R. J., & Margon, B. 2005, AJ, 130, 2439 [NASA ADS] [CrossRef] [Google Scholar]
  89. Szegedy, C., Liu, W., Jia, Y., et al. 2015, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1 [Google Scholar]
  90. Tchekhovskoy, A., Narayan, R., & McKinney, J. C. 2011, MNRAS, 418, L79 [NASA ADS] [CrossRef] [Google Scholar]
  91. van der Walt, S., Colbert, S. C., & Varoquaux, G. 2011, Comput. Sci. Eng., 13, 22 [Google Scholar]
  92. Vasconcellos, E. C., de Carvalho, R. R., Gal, R. R., et al. 2011, AJ, 141, 189 [NASA ADS] [CrossRef] [Google Scholar]
  93. Walker, R. C., Hardee, P. E., Davies, F. B., Ly, C., & Junor, W. 2018, ApJ, 855, 128 [NASA ADS] [CrossRef] [Google Scholar]
  94. Walsh, J. L., Barth, A. J., Ho, L. C., & Sarzi, M. 2013, ApJ, 770, 86 [NASA ADS] [CrossRef] [Google Scholar]
  95. Weir, N., Fayyad, U. M., Djorgovski, S. G., & Roden, J. 1995, PASP, 107, 1243 [NASA ADS] [CrossRef] [Google Scholar]
  96. Zeiler, M. D., & Fergus, R. 2014, European Conference on Computer Vision (Springer), 818 [Google Scholar]

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