Multiwavelength classification of X-ray selected galaxy cluster candidates using convolutional neural networks

@inproceedings{Kosiba2020MultiwavelengthCO,
  title={Multiwavelength classification of X-ray selected galaxy cluster candidates using convolutional neural networks},
  author={Matej Kosiba and Maggie Lieu and Bruno Altieri and Nicolas Clerc and Lorenzo Faccioli and Sarah Kendrew and Ivan Valtchanov and Tatyana Sadibekova and Marguerite Pierre and Filip Hroch and Norbert Werner and Luk'avs Burget and Christian Garrel and Elias Koulouridis and Evelina R. Gaynullina and Mona Molham and Miriam E. Ramos-Ceja and A. V. Khalikova},
  year={2020}
}
  • Matej Kosiba, Maggie Lieu, +15 authors A. V. Khalikova
  • Published 2020
  • Physics
  • Galaxy clusters appear as extended sources in XMM-Newton images, but not all extended sources are clusters. So, their proper classification requires visual inspection with optical images, which is a slow process with biases that are almost impossible to model. We tackle this problem with a novel approach, using convolutional neural networks (CNNs), a state-of-the-art image classification tool, for automatic classification of galaxy cluster candidates. We train the networks on combined XMM… CONTINUE READING

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