DeepVoid: A Deep Learning Void Detector

Abstract We present DeepVoid, an application of deep learning trained on a physical definition of cosmic voids to detect voids in density fields and galaxy distributions. By semantically segmenting the IllustrisTNG simulation volume using the tidal tensor, we train a deep convolutional neural network to classify local structure using a U-Net architecture for training and prediction. The model achieves a void F1 score of 0.96 and a Matthews correlation coefficient over all structural classes of 0.81 for dark matter particles in IllustrisTNG with interparticle spacing of λ = 0.33 h −1 Mpc. We then apply the machine learning technique of curriculum learning to enable the model to classify structure in data with significantly larger intertracer separation. At the highest tracer separation tested, λ = 10 h −1 Mpc, the model achieves a void F1 score of 0.89 and a Matthews correlation coefficient of 0.6 on IllustrisTNG subhalos.