Deep learning approach for identification of H ii regions during reionization in 21-cm observations

@inproceedings{Bianco2021DeepLA,
  title={Deep learning approach for identification of H ii regions during reionization in 21-cm observations},
  author={M. Bianco and Sambit K. Giri and Ilian T Iliev and Garrelt Mellema},
  year={2021}
}
The upcoming Square Kilometre Array (SKA-Low)will map the distribution of neutral hydrogen during reionization and produce a tremendous amount of 3D tomographic data. These images cubes will be subject to instrumental limitations, such as noise and limited resolution. Here we present SegU-Net, a stable and reliable method for identifying neutral and ionized regions in these images. SegU-Net is a U-Net architecture based convolutional neural network (CNN) for image segmentation. It is capable of… Expand
Reconstructing Cosmic Polarization Rotation with ResUNet-CMB
TLDR
The ResUNet-CMB convolutional neural network is extended to reconstruct anisotropic cosmic polarization rotation in the presence of gravitational lensing and patchy reionization, and it is shown that the network simultaneously reconstructs all three effects with variance that is lower than that from the standard quadratic estimator nearly matching the performance of an iterative reconstruction method. Expand
SKA-Low Intensity Mapping Pathfinder Updates: Deeper 21 cm Power Spectrum Limits from Improved Analysis Frameworks
The Square Kilometre Array (SKA) is a planned radio interferometer of unprecedented scale that will revolutionize low-frequency radio astronomy when completed. In particular, one of its core scienceExpand

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