EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues Volume 683 (March 2024) A&A, 683 (2024) A185 References
Open Access
Issue
A&A
Volume 683, March 2024
Article Number A185
Number of page(s) 12
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202348566
Published online 19 March 2024
  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. Abdollahi, S., Acero, F., Ackermann, M., et al. 2020, ApJS, 247, 33 [Google Scholar]
  3. Abiodun, O. I., Jantan, A., Omolara, A. E., et al. 2018, Heliyon, 4, e00938 [CrossRef] [PubMed] [Google Scholar]
  4. Alarfaj, F. K., Khan, N. A., Sulaiman, M., & Alomair, A. M. 2022, Symmetry, 14, 2482 [NASA ADS] [CrossRef] [Google Scholar]
  5. Amato, E., & Olmi, B. 2021, Universe, 7, 448 [NASA ADS] [CrossRef] [Google Scholar]
  6. Becker, P. A., & Wolff, M. T. 2022, ApJ, 939, 67 [NASA ADS] [CrossRef] [Google Scholar]
  7. Bégué, D., Sahakyan, N., Dereli Bégué, H., et al. 2023, ApJ, submitted [arXiv:2311.02979] [Google Scholar]
  8. Bishop, C. M. 1995, Neural Networks for Pattern Recognition (Oxford: Clarendon Press) [Google Scholar]
  9. Biteau, J., Prandini, E., Costamante, L., et al. 2020, Nat. Astron., 4, 124 [Google Scholar]
  10. Blandford, R., Meier, D., & Readhead, A. 2019, ARA&A, 57, 467 [NASA ADS] [CrossRef] [Google Scholar]
  11. Bloom, S. D., & Marscher, A. P. 1996, ApJ, 461, 657 [NASA ADS] [CrossRef] [Google Scholar]
  12. Buchner, J. 2021, J. Open Source Softw., 6, 3001 [CrossRef] [Google Scholar]
  13. Celotti, A., & Ghisellini, G. 2008, MNRAS, 385, 283 [NASA ADS] [CrossRef] [Google Scholar]
  14. Cerruti, M., Zech, A., Boisson, C., & Inoue, S. 2015, MNRAS, 448, 910 [Google Scholar]
  15. Cerruti, M., Kreter, M., Petropoulou, M., et al. 2022, in 37th International Cosmic Ray Conference, 979 [Google Scholar]
  16. Cho, K., Van Merriënboer, B., Bahdanau, D., & Bengio, Y. 2014, arXiv e-prints [arXiv:1409.1259] [Google Scholar]
  17. Chollet, F., et al. 2015, Keras, https://keras.io [Google Scholar]
  18. Dimitrakoudis, S., Mastichiadis, A., Protheroe, R. J., & Reimer, A. 2012, A&A, 546, A120 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  19. Draper, N. R., & Smith, H. 1998, Applied Regression Analysis, 326 (John Wiley & Sons) [Google Scholar]
  20. Finke, J. D., Dermer, C. D., & Böttcher, M. 2008, ApJ, 686, 181 [NASA ADS] [CrossRef] [Google Scholar]
  21. Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306 [Google Scholar]
  22. Gao, S., Pohl, M., & Winter, W. 2017, ApJ, 843, 109 [NASA ADS] [CrossRef] [Google Scholar]
  23. Gasparyan, S., Bégué, D., & Sahakyan, N. 2022, MNRAS, 509, 2102 [Google Scholar]
  24. Giommi, P., Padovani, P., Oikonomou, F., et al. 2020, A&A, 640, L4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  25. Glorot, X., & Bengio, Y. 2010, in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings, 249 [Google Scholar]
  26. Hillas, A. M. 1984, ARA&A, 22, 425 [Google Scholar]
  27. Hochreiter, S., & Schmidhuber, J. 1997, Neural Comput., 9, 1735 [CrossRef] [Google Scholar]
  28. Hodges Jr, J. 1958, Arkiv för matematik, 3, 469 [CrossRef] [Google Scholar]
  29. Hovatta, T., & Lindfors, E. 2019, New Astron. Rev., 87, 101541 [CrossRef] [Google Scholar]
  30. Karaferias, A. S., Vasilopoulos, G., Petropoulou, M., et al. 2023, MNRAS, 520, 281 [NASA ADS] [CrossRef] [Google Scholar]
  31. Kingma, D. P., & Ba, J. 2014, arXiv e-prints [arXiv:1412.6988] [Google Scholar]
  32. Maraschi, L., Ghisellini, G., & Celotti, A. 1992, ApJ, 397, L5 [CrossRef] [Google Scholar]
  33. Mastichiadis, A., & Kirk, J. G. 1995, A&A, 295, 613 [NASA ADS] [Google Scholar]
  34. Mastichiadis, A., & Kirk, J. G. 1997, A&A, 320, 19 [NASA ADS] [Google Scholar]
  35. Mastichiadis, A., Protheroe, R. J., & Kirk, J. G. 2005, A&A, 433, 765 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  36. Mastichiadis, A., Florou, I., Kefala, E., Boula, S. S., & Petropoulou, M. 2020, MNRAS, 495, 2458 [NASA ADS] [CrossRef] [Google Scholar]
  37. McCulloch, W. S., & Pitts, W. 1943, Bull. Math. Biophys., 5, 115 [CrossRef] [Google Scholar]
  38. Padovani, P., Alexander, D. M., Assef, R. J., et al. 2017, A&ARv, 2, 25 [Google Scholar]
  39. Paiano, S., Falomo, R., Padovani, P., et al. 2020, MNRAS, 495, L108 [Google Scholar]
  40. Paliya, V. S., Böttcher, M., Olmo-García, A., et al. 2020, ApJ, 902, 29 [NASA ADS] [CrossRef] [Google Scholar]
  41. Paszke, A., Gross, S., Massa, F., et al. 2019, in Advances in Neural Information Processing Systems 32 (Curran Associates, Inc.), 8024 [Google Scholar]
  42. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, J. Mach. Learn. Res., 12, 2825 [Google Scholar]
  43. Petropoulou, M., & Mastichiadis, A. 2012, MNRAS, 421, 2325 [NASA ADS] [CrossRef] [Google Scholar]
  44. Petropoulou, M., & Mastichiadis, A. 2018, MNRAS, 477, 2917 [Google Scholar]
  45. Petropoulou, M., Dimitrakoudis, S., Padovani, P., Mastichiadis, A., & Resconi, E. 2015a, MNRAS, 448, 2412 [Google Scholar]
  46. Petropoulou, M., Piran, T., & Mastichiadis, A. 2015b, MNRAS, 452, 3226 [Google Scholar]
  47. Petropoulou, M., Oikonomou, F., Mastichiadis, A., et al. 2020, ApJ, 899, 113 [NASA ADS] [CrossRef] [Google Scholar]
  48. Readhead, A. C. S. 1994, ApJ, 426, 51 [Google Scholar]
  49. Rudolph, A., Petropoulou, M., Bošnjak, Ž., & Winter, W. 2023, ApJ, 950, 28 [NASA ADS] [CrossRef] [Google Scholar]
  50. Senin, P. 2008, Dynamic Time Warping Algorithm Review (Honolulu, USA: Information and Computer Science Department) 855, 40 [Google Scholar]
  51. Stathopoulos, S. I., Petropoulou, M., Vasilopoulos, G., & Mastichiadis, A. 2024, A&A, in press, https://doi.org/10.1051/0004-6361/202347277 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  52. Strogatz, S. H. 2000, Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry and Engineering (Westview Press) [Google Scholar]
  53. The Theano Development Team, Al-Rfou, R., Alain, G., et al. 2016, arXiv e-prints [arXiv: 1605.02600] [Google Scholar]
  54. Wang, S., Fan, K., Luo, N., et al. 2019, Nat. Commun., 10, 4354 [NASA ADS] [CrossRef] [Google Scholar]
  55. West, B. F., Wolfram, K. D., & Becker, P. A. 2017, ApJ, 835, 129 [NASA ADS] [CrossRef] [Google Scholar]
  56. Wiecha, P. R., & Muskens, O. L. 2020, Nano Lett., 20, 329 [NASA ADS] [CrossRef] [Google Scholar]
  57. Wolff, M. T., Becker, P. A., Gottlieb, A. M., et al. 2016, ApJ, 831, 194 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.