Improving Convolutional Neural Networks for Cosmological Fields with Random Permutation
arxiv(2024)
摘要
Convolutional Neural Networks (CNNs) have recently been applied to
cosmological fields – weak lensing mass maps and galaxy maps. However,
cosmological maps differ in several ways from the vast majority of images that
CNNs have been tested on: they are stochastic, typically low signal-to-noise
per pixel, and with correlations on all scales. Further, the cosmology goal is
a regression problem aimed at inferring posteriors on parameters that must be
unbiased. We explore simple CNN architectures and present a novel approach of
regularization and data augmentation to improve its performance for lensing
mass maps. We find robust improvement by using a mixture of pooling and
shuffling of the pixels in the deep layers. The random permutation regularizes
the network in the low signal-to-noise regime and effectively augments the
existing data. We use simulation-based inference (SBI) to show that the model
outperforms CNN designs in the literature. We find a 30
constraints of the S_8 parameter for simulated Stage-III surveys, including
systematic uncertainties such as intrinsic alignments. We explore various
statistical errors corresponding to next-generation surveys and find comparable
improvements. We expect that our approach will have applications to other
cosmological fields as well, such as galaxy maps or 21-cm maps.
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