Towards an optimal extraction of cosmological parameters from galaxy cluster surveys using convolutional neural networks

Key Points

• Accurate extraction of cosmological parameters enhances understanding of the universe's evolution, with implications for future research.
• The method shows improved data extraction from galaxy cluster surveys using convolutional neural networks, enhancing precision.
• Assessment using convolutional neural networks leads to significant advancements in interpreting complex astrophysical data.
• This approach highlights the potential for machine learning to refine astronomy methodology, paving the way for better studies.

Abstract