Classifying the large-scale structure of the universe with deep neural networks

We present the first application of deep neural networks to the semantic segmentation of cosmological filaments and walls in the large-scale structure of the universe. Our results are based on a deep convolutional neural network (CNN) with a U-Net architecture trained using an existing state-of-the-art manually guided segmentation method. We successfully trained and tested a U-Net with a Voronoi model and an N-body simulation. The predicted segmentation masks from the Voronoi model have a Dice coefficient of 0.95 and 0.97 for filaments and masks, respectively. The predicted segmentation masks from the N-body simulation have a Dice coefficient of 0.78 and 0.72 for walls and filaments, respectively. The relatively lower Dice coefficient in the filament mask is the result of filaments that were predicted by the U-Net model but were not present in the original segmentation mask. Our results show that for a well-defined data set such as the Voronoi model the U-Net has excellent performance. In the case of the N-body data set, the U-Net produced a filament mask of higher quality than the segmentation mask obtained from a state-of-the art method. The U-Net performs better than the method used to train it, being able to find even the tenuous filaments that the manually guided segmentation failed to identify. The U-Net presented here can process a 5123 volume in a few minutes and without the need of complex pre-processing. Deep CNN have great potential as efficient and accurate analysis tools for the next generation large-volume computer N-body simulations and galaxy surveys.