Deep learning of galaxy cluster members through panchromatic HST imaging and extensive spectroscopy.

The upcoming next-generation of extensive and data-intensive surveys are going to produce a vast amount of data, which can be efficiently dealt with Machine Learning methods to explore possible correlations within the multi-dimensional parameter space. We explored classification capabilities of Convolution Neural Networks (CNN) to identify galaxy Cluster Members (CLMs), by using Hubble Space Telescope images of 15 galaxy clusters at redshift 0.19

Keywords:

• Artificial intelligence
• Galaxy cluster
• Galaxy
• Deep learning
• Photometry (optics)
• Astrophysics
• Artificial neural network
• Convolution
• Physics
• Luminosity
• Redshift

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