2025/04/29 by Kumagai, Sam, Vogeley, Michael S., Aragon-Calvo, Miguel A. +3
#FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM)
paper · doi:10.48550/arxiv.2504.21134
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 curricular 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.