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Home All issues Volume 669 (January 2023) A&A, 669 (2023) A42 References
Open Access
Issue
A&A
Volume 669, January 2023
Article Number A42
Number of page(s) 9
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202142525
Published online 04 January 2023
  1. Aasi, J., Abbott, B. P., Abbott, R., et al. 2015, Class. Quant. Grav., 32, 074001 [Google Scholar]
  2. Abbott, B. P., Abbott, R., Abbott, T. D., et al. 2016a, Class. Quant. Grav., 33, 134001 [NASA ADS] [CrossRef] [Google Scholar]
  3. Abbott, B. P., Abbott, R., Abbott, T. D., et al. 2016b, Phys. Rev. Lett., 116, 061102 [Google Scholar]
  4. Abbott, B. P., Abbott, R., Abbott, T. D., et al. 2017, Phys. Rev. Lett., 119, 161101 [Google Scholar]
  5. Abbott, B. P., Abbott, R., Abbott, T. D., et al. 2019, Phys. Rev. X, 9, 031040 [Google Scholar]
  6. Abbott, R., Abbott, T. D., Abraham, S., et al. 2021a, Phys. Rev. X 11, 021053 [Google Scholar]
  7. Abbott, R., Abbott, T. D., Abraham, S., et al. 2021b, SoftwareX, 13, 100658 [NASA ADS] [CrossRef] [Google Scholar]
  8. Acernese, F., Agathos, M., Agatsuma, K., et al. 2015, Class. Quant. Grav., 32, 024001 [Google Scholar]
  9. Akutsu, T., Ando, M., Arai, K., et al. 2020, Progr. Theor. Exp. Phys., 2021 [Google Scholar]
  10. Allen, B., Anderson, W. G., Brady, P. R., Brown, D. A., & Creighton, J. D. E. 2012, Phys. Rev. D, 85, 122006 [NASA ADS] [CrossRef] [Google Scholar]
  11. Andresen, H., Müller, B., Müller, E., & Janka, H. T. 2017, MNRAS, 468, 2032 [NASA ADS] [CrossRef] [Google Scholar]
  12. Astone, P., Cerdá-Durán, P., Di Palma, I., et al. 2018, Phys. Rev. D., 98, 122002 [NASA ADS] [CrossRef] [Google Scholar]
  13. Baker, P. T., Caudill, S., Hodge, K. A., et al. 2015, Phys. Rev. D, 91, 062004 [NASA ADS] [CrossRef] [Google Scholar]
  14. Cappellaro, E., Turatto, M., Benetti, S., et al. 1993, A&A, 273, 383 [NASA ADS] [Google Scholar]
  15. Cavaglià, M., Gaudio, S., Hansen, T., et al. 2020, Mach. Learn.: Sci. Technol., 1, 015005 [CrossRef] [Google Scholar]
  16. Chan, M. L., Heng, I. S., & Messenger, C. 2020, Phys. Rev. D, 102, 043022 [NASA ADS] [CrossRef] [Google Scholar]
  17. Chua, A. J., & Vallisneri, M. 2020, Phys. Rev. Lett., 124, 041102 [NASA ADS] [CrossRef] [Google Scholar]
  18. Cuoco, E., Calamai, G., Fabbroni, L., et al. 2001, Class. Quant. Grav., 18, 1727 [NASA ADS] [CrossRef] [Google Scholar]
  19. Cuoco, E., Razzano, M., & Utina, A. 2018, in 26th European Signal Processing Conference (EUSIPCO), 2648 [CrossRef] [Google Scholar]
  20. Cuoco, E., Powell, J., Cavaglià, M., et al. 2020, Mach. Learn. Sci. Technol. [Google Scholar]
  21. Gabbard, H., Williams, M., Hayes, F., & Messenger, C. 2018, Phys. Rev. Lett., 120, 141103 [NASA ADS] [CrossRef] [Google Scholar]
  22. Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., et al. 2014, Gener. Adversarial Netw. [Google Scholar]
  23. Green, S. R., Simpson, C., & Gair, J. 2020, Phys. Rev. D, 102, 104057 [NASA ADS] [CrossRef] [Google Scholar]
  24. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. 2017, Improved Training of Wasserstein GANs [Google Scholar]
  25. Haegel, L., & Husa, S. 2020, Class. Quant. Grav., 37, 135005 [NASA ADS] [CrossRef] [Google Scholar]
  26. Harry, G. M. 2010, Class. Quant. Grav., 27, 084006 [NASA ADS] [CrossRef] [Google Scholar]
  27. Hild, S., Abernathy, M., Acernese, F., et al. 2011, Class. Quant. Grav., 28, 094013 [Google Scholar]
  28. Hochreiter, S., & Schmidhuber, J. 1997, Neural Comput., 9, 1735 [CrossRef] [Google Scholar]
  29. Hüsken, M., & Stagge, P. 2003, Neurocomputing, 50, 223 [CrossRef] [Google Scholar]
  30. Iess, A., Cuoco, E., Morawski, F., & Powell, J. 2020, Mach. Learn. Sci. Technol., 1, 025014 [CrossRef] [Google Scholar]
  31. Kim, K., Li, T. G. F., Lo, R. K. L., Sachdev, S., & Yuen, R. S. H. 2020, Phys. Rev. D, 101, 083006 [NASA ADS] [CrossRef] [Google Scholar]
  32. Klimenko, S., & Mitselmakher, G. 2004, Class. Quant. Grav., 21, S1819 [NASA ADS] [CrossRef] [Google Scholar]
  33. Klimenko, S., Vedovato, G., Drago, M., et al. 2016, Phys. Rev. D, 93, 042004 [NASA ADS] [CrossRef] [Google Scholar]
  34. López, M., Di Palma, I., Drago, M., Cerdá-Durán, P., & Ricci, F. 2021, Phys. Rev. D, 103, 063011 [CrossRef] [Google Scholar]
  35. Morawski, F., Bejger, M., & Ciecielag, P. 2020, Mach. Learn. Sci. Technol., 1, 025016 [CrossRef] [Google Scholar]
  36. Mukund, N., Abraham, S., Kandhasamy, S., Mitra, S., & Philip, N. S. 2017, Phys. Rev. D, 95, 104059 [NASA ADS] [CrossRef] [Google Scholar]
  37. Müller, B., Janka, H.-T., & Dimmelmeier, H. 2010, ApJS, 189, 104 [CrossRef] [Google Scholar]
  38. Nicolaou, C., Vaidya, A., Dzogang, F., Wardrope, D., & Konstantinidis, N. 2019, in 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), 351 [CrossRef] [Google Scholar]
  39. Powell, J., & Müller, B. 2019, MNRAS, 487, 1178 [NASA ADS] [CrossRef] [Google Scholar]
  40. Powell, J., & Müller, B. 2020, MNRAS, 494, 4665 [NASA ADS] [CrossRef] [Google Scholar]
  41. Punturo, M., Abernathy, M., Acernese, F., et al. 2010, Class. Quant. Grav., 27, 194002 [Google Scholar]
  42. Radice, D., Morozova, V., Burrows, A., Vartanyan, D., & Nagakura, H. 2019, ApJ, 876, L9 [NASA ADS] [CrossRef] [Google Scholar]
  43. Rampp, M., & Janka, H. T. 2002, A&A, 396, 361 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  44. Razzano, M., & Cuoco, E. 2018, Class. Quant. Grav., 35, 095016 [NASA ADS] [CrossRef] [Google Scholar]
  45. Rozwadowska, K., Vissani, F., & Cappellaro, E. 2021, New Astron., 83, 101498 [NASA ADS] [CrossRef] [Google Scholar]
  46. Schutz, B. F. 2011, Class. Quant. Grav., 28, 125023 [Google Scholar]
  47. Skinner, M. A., Dolence, J. C., Burrows, A., Radice, D., & Vartanyan, D. 2019, ApJS, 241, 7 [NASA ADS] [CrossRef] [Google Scholar]
  48. Somiya, K. 2012, Class. Quant. Grav., 29, 124007 [NASA ADS] [CrossRef] [Google Scholar]
  49. Tauris, T. M., Langer, N., & Podsiadlowski, P. 2015, MNRAS, 451, 2123 [NASA ADS] [CrossRef] [Google Scholar]
  50. Torres-Forné, A., Cuoco, E., Font, J. A., & Marquina, A. 2020, Phys. Rev. D, 102, 023011 [CrossRef] [Google Scholar]
  51. van den Bergh, S., & Tammann, G. A. 1991, ARA&A, 29, 363 [NASA ADS] [CrossRef] [Google Scholar]
  52. Varma, V., Gerosa, D., Stein, L. C., Hébert, F., & Zhang, H. 2019, Phys. Rev. Lett., 122, 011101 [NASA ADS] [CrossRef] [Google Scholar]
  53. Wei, W., & Huerta, E. 2020, Phys. Lett. B, 800, 135081 [NASA ADS] [CrossRef] [Google Scholar]
  54. Williams, M. J., Veitch, J., & Messenger, C. 2021, Phys. Rev. D, 103, 103006 [NASA ADS] [CrossRef] [Google Scholar]
  55. Yu, N., Davis, L., & Fritz, M. 2019, Attributing Fake Images to GANs: Learning and Analyzing GAN Fingerprints [Google Scholar]
  56. Zevin, M., Coughlin, S., Bahaadini, S., et al. 2017, Class. Quant. Grav., 34, 064003 [NASA ADS] [CrossRef] [Google Scholar]

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