Deep Neural Networks for Source Detection in Radio Astronomical Maps
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-34167-0_27
Reference18 articles.
1. Magro, D., Zarb Adami, K., DeMarco, A., Riggi, S., Sciacca, E.: A comparative study of convolutional neural networks for the detection of strong gravitational lensing. Month. Not. R. Astron. Soc. 505(4), 6155–6165 (2021). https://doi.org/10.1093/mnras/stab1635
2. Ralph, N.O., Norris, R.P., Fang, G., Park, L.A.F., Galvin, T.J., Alger, M.J., Andernach, H., Lintott, C., Rudnick, L., Shabala, S., Wong, O.I.: Radio galaxy zoo: unsupervised clustering of convolutionally auto-encoded radio-astronomical images. Publ. Astron. Soc. Pac. 131(1004), 108011 (2019). https://doi.org/10.1088/1538-3873/ab213d
3. Karypidou, S., Georgousis, I., Papakostas, G.A.: Computer vision for astronomical image analysis. In: 2021 IEEE International Conference on Progress in Informatics and Computing (PIC), pp. 94–101 (2021). https://doi.org/10.1109/PIC53636.2021.9687023
4. Hancock, P.J., Trott, C.M., Hurley-Walker, N.: Source finding in the era of the ska (precursors): Aegean 2.0. Publ. Astron. Soc. Australia 35, e011 (2018). https://doi.org/10.1017/pasa.2018.3
5. Mohan, N.R., Rafferty, D.A.: PyBDSF: python blob detection and source finder. Astrophysics Source Code Library pp. ascl–1502 (2015)
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