Clusternets: A deep learning approach to probe clustering dark energy

<p>This directory contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled &quot;Clusternets: A deep learning approach to probe clustering dark energy&quot;</p> <p>Directories</p> <ul> <li><strong>Codes</strong>: This directory contains different codes used to generate and post-process the simulation data, including k-evolution, gevolution, Pylians, CLASS, and&nbsp;LATfield2.</li> <li><strong>Final figures and data</strong>: This directory includes the data for the power spectra, simulation settings and Jupyter notebooks to produce the figures.</li> </ul> <p>How to Use</p> <ol> <li>Download the files to your local machine.</li> <li>Navigate to the directory where the files are saved.</li> <li>Install the necessary packages</li> <li>Navigate to the &quot;<strong>Final figures and data</strong>&quot; directory and open the Jupyter notebooks in your preferred environment.</li> <li>Run the cells in the notebooks to reproduce the figures.</li> <li>Navigate to the &quot;<strong>Codes</strong>&quot; directory and use the appropriate code to generate and post-process the simulation data.</li> <li>Use the simulation setting&nbsp;files in the &quot;<strong>Final figures and data</strong>&quot; directory to replicate the simulations.</li> </ol> <p>Note: Some of the simulations may require high computational resources and may take a significant amount of time to run.<br> <br> If you have any feedback or request feel free to email&nbsp;chgeniamirsbu@gmail.com and farbod.hassani@gmail.com</p>