Free Access
Volume 642, October 2020
Article Number A26
Number of page(s) 7
Section Numerical methods and codes
Published online 30 September 2020
  1. Barsdell, B. R., Bailes, M., Barnes, D. G., & Fluke, C. J. 2012, MNRAS, 422, 379 [CrossRef] [Google Scholar]
  2. CHIME/FRB Collaboration (Amiri, M., et al.) 2019a, Nature, 566, 235 [NASA ADS] [CrossRef] [Google Scholar]
  3. CHIME/FRB Collaboration (Andersen, B. C., et al.) 2019b, ApJ, 885, L24 [NASA ADS] [CrossRef] [Google Scholar]
  4. Connor, L., & van Leeuwen, J. 2018, AJ, 156, 256 [NASA ADS] [CrossRef] [Google Scholar]
  5. Cordes, J. M., & Chatterjee, S. 2019, ARA&A, 57, 417 [NASA ADS] [CrossRef] [Google Scholar]
  6. Devine, T. R., Goseva-Popstojanova, K., & McLaughlin, M. 2016, MNRAS, 459, 1519 [NASA ADS] [CrossRef] [Google Scholar]
  7. Farah, W., Flynn, C., Bailes, M., et al. 2019, MNRAS, 488, 2989 [NASA ADS] [CrossRef] [Google Scholar]
  8. Goodfellow, I., Bengio, Y., & Courville, A. 2016, Deep Learning (MIT Press) [Google Scholar]
  9. Hobbs, G., Miller, D., Manchester, R. N., et al. 2011, PASA, 28, 202 [NASA ADS] [CrossRef] [Google Scholar]
  10. Hotan, A. W., van Straten, W., & Manchester, R. N. 2004, PASA, 21, 302 [NASA ADS] [CrossRef] [Google Scholar]
  11. Kaspi, V., Manchester, R., & Lyne, A. 2016a, Parkes Observations for Project P269 Semester 2001JANT [Google Scholar]
  12. Kaspi, V., Manchester, R., & Lyne, A. 2016b, Parkes Observations for Project P269 Semester 2000OCTT [Google Scholar]
  13. Li, D., Wang, P., Qian, L., et al. 2018, IEEE Microw. Mag., 19, 112 [CrossRef] [Google Scholar]
  14. Lorimer, D. R., Bailes, M., McLaughlin, M. A., Narkevic, D. J., & Crawford, F. 2007, Science, 318, 777 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  15. Lyne, A., Manchester, R., & Camilo, F. 2012a, Parkes Observations for Project P268 Semester 1997AUGT [Google Scholar]
  16. Lyne, A., Kramer, M., & Manchester, R. 2012b, Parkes Observations for Project P268 Semester 2001MAYT [Google Scholar]
  17. Manchester, R. N., Fan, G., Lyne, A. G., Kaspi, V. M., & Crawford, F. 2006, ApJ, 649, 235 [NASA ADS] [CrossRef] [Google Scholar]
  18. McLaughlin, M. A., Lyne, A. G., Lorimer, D. R., et al. 2006, Nature, 439, 817 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  19. Men, Y. P., Luo, R., Chen, M. Z., et al. 2019, MNRAS, 488, 3957 [CrossRef] [Google Scholar]
  20. Michilli, D., Hessels, J. W. T., Lyon, R. J., et al. 2018, MNRAS, 480, 3457 [NASA ADS] [CrossRef] [Google Scholar]
  21. Mickaliger, M. B., Lorimer, D. R., Boyles, J., et al. 2012, ApJ, 759, 127 [CrossRef] [Google Scholar]
  22. Montavon, G., Samek, W., & Muller, K.-R. 2018, Digit. Signal Process., 73, 1 [CrossRef] [Google Scholar]
  23. Norris, R. P., Salvato, M., Longo, G., et al. 2019, PASP, 131, 108004 [CrossRef] [Google Scholar]
  24. Pan, Z., Hobbs, G., Li, D., et al. 2016, MNRAS, 459, L26 [CrossRef] [Google Scholar]
  25. Petroff, E., Keane, E. F., Barr, E. D., et al. 2015, MNRAS, 451, 3933 [CrossRef] [Google Scholar]
  26. Platts, E., Weltman, A., Walters, A., et al. 2019, Phys. Rep., 821, 1 [NASA ADS] [CrossRef] [Google Scholar]
  27. Ransom, S. M. 2001, PhD Thesis, Harvard University, USA [Google Scholar]
  28. Simonyan, K., Vedaldi, A., & Zisserman, A. 2013, ArXiv e-prints [arXiv:1312.6034] [Google Scholar]
  29. Spitler, L. G., Scholz, P., Hessels, J. W. T., et al. 2016, Nature, 531, 202 [NASA ADS] [CrossRef] [Google Scholar]
  30. Sundararajan, M., Taly, A., & Yan, Q. 2017, Proceedings of the 34th International Conference on Machine Learning – Volume 70, ICML’17 (, 3319 [Google Scholar]
  31. Zhang, Y. G., Gajjar, V., Foster, G., et al. 2018a, ApJ, 866, 149 [NASA ADS] [CrossRef] [Google Scholar]
  32. Zhang, S.-B., Dai, S., Hobbs, G., et al. 2018b, MNRAS, 479, 1836 [CrossRef] [Google Scholar]
  33. Zhang, S.-B., Hobbs, G., Dai, S., et al. 2019, MNRAS, 484, L147 [NASA ADS] [CrossRef] [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.