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We propose a novel approach for mitigating radio frequency interference (RFI) signals in radio data using the latest advances in deep learning.
1958
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W. Baan, P. Fridman, R. Millenaar, Radio frequency interference mitigation at the westerbork synthesis radio telescope: Algorithms, test observations, and system implementation, The Astronomical Journal 128 (2) (2004) 933
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R. Collobert, J. Weston, A unified architecture for natural language processing: Deep neural networks with multitask learning, in: Proceedings of the 25th international conference on Machine learning, ACM, 2008, pp. 160–167
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A. Offringa, A. de Bruyn, M. Biehl, S. Zaroubi, G. Bernardi, V. Pandey, Post-correlation radio frequency interference classification methods, Monthly Notices of the Royal Astronomical Society 405 (1) (2010) 155–167
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L. W. Peck, D. M. Fenech, Serpent: Automated reduction and rfi-mitigation software for e-merlin, Astronomy and Computing 2 (2013) 54–66
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Z. S. Ali, A. R. Parsons, H. Zheng, J. C. Pober, A. Liu, J. E. Aguirre, R. F. Bradley, G. Bernardi, C. L. Carilli, C. Cheng, et al., Paper-64 constraints on reionization: The 21 cm power spectrum at z= 8.4, The Astrophysical Journal 809 (1) (2015) 61
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G. Paciga, J. G. Albert, K. Bandura, T.-C. Chang, Y. Gupta, C. Hirata, J. Odegova, U.-L. Pen, J. B. Peterson, J. Roy, et al., A simulation-calibrated limit on the h i power spectrum from the gmrt epoch of reionization experiment, Monthly Notices of the Royal Astronomical Society (2013) stt753
2013
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R. Battye, I. Browne, C. Dickinson, G. Heron, B. Maffei, A. Pourtsidou, H i intensity mapping: a single dish approach, Monthly Notices of the Royal Astronomical Society (2013) stt1082
2013
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J. Zhao, X. Zou, F. Weng, Windsat radio-frequency interference signature and its identification over greenland and antarctic, IEEE Transactions on Geoscience and Remote Sensing 51 (9) (2013) 4830–4839
2013
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Cited in the paper.
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2015
Later among the works it cites.
O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2015, pp. 234–241
2015
Later among the works it cites.
C. J. Wolfaardt, Machine learning approach to radio frequency interference (rfi) classification in radio astronomy, Ph.D. thesis, Stellenbosch University (2016)
2016
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