Deep Learning for Galaxy Cluster Structural Parameter Estimation

This study demonstrates the effectiveness of Convolutional Neural Networks (CNNs) for inferring galaxy cluster structural parameters from weak gravitational lensing observations. The use of three different CNN architectures (VGG-Net, Inception-v4, Inception-ResNet-v2) allows for a comprehensive evaluation of their performance. The high accuracy achieved for primary properties, such as virial mass and NFW concentrations, is particularly encouraging. The comparison with traditional shear profile fitting highlights the advantages of CNNs in terms of accuracy and efficiency. The systematic underestimation of substructure properties suggests that further refinement of the CNN architectures is needed to improve the accuracy of these estimates. The scalability of CNNs makes them well-suited for processing the large data volumes expected from upcoming surveys. Future research could focus on developing more sophisticated CNN architectures and incorporating additional data sources to improve the accuracy of parameter estimation. The development and deployment of CNNs for galaxy cluster analysis demonstrates the increasing role of machine learning in cosmology and its potential to accelerate scientific discovery.

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