Using Cartesian slice plots of a cosmological simulation as input of a convolutional neural network
Guillermo Arreaga-Garcia

TL;DR
This paper demonstrates that Cartesian slice plots derived from cosmological simulations can effectively serve as input images for CNNs to classify cosmic web structures, showing promising accuracy.
Contribution
It introduces a novel approach of using Cartesian slice plots from cosmological simulations as CNN input for structure classification, with multiple models tested for effectiveness.
Findings
CNN achieves acceptable classification accuracy
Different visualization volumes impact model performance
Cartesian plots contain sufficient information for cosmic web identification
Abstract
Using a uniform partitioning of cubic cells, we cover the total volume of a CDM cosmological simulation based on particles. We define a visualisation cell as a spatial extension of the cubic cell, so that we collect all simulation particles contained in this visualisation cell to create a series of Cartesian plots in which the over-density of matter is clearly visible. We then use these plots as input to a convolutional neural network (CNN) based on the Keras library and TensorFlow for image classification. To assign a class to each plot, we approximate the Hessian of the gravitational potential in the centre of the cubic cells. Each selected cubic cell is then assigned a label of 1,2 or 3, depending on the number of positive eigenvalues obtained for the Householder reduction of the Hessian matrix. We apply the CNN to several models, including two models with different…
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Taxonomy
TopicsComputational Physics and Python Applications
