A Hybrid Deep Learning Approach to Cosmological Constraints From Galaxy Redshift Surveys

Abstract

We present a deep machine learning (ML)-based technique for accurately determining ${\sigma }_8$ and ${\Omega }_m$ from mock 3D galaxy surveys. The mock surveys are built from the AbacusCosmos suite of $N$-body simulations, which comprises 40 cosmological volume simulations spanning a range of cosmological models, and we account for uncertainties in galaxy formation scenarios through the use of generalized halo occupation distributions (HODs). We explore a trio of ML models: a 3D convolutional neural network (CNN), a power-spectrum-based fully connected network, and a hybrid approach that merges the two to combine physically motivated summary statistics with flexible CNNs. We describe best practices for training a deep model on a suite of matched-phase simulations and we test our model on a completely independent sample that uses previously unseen initial conditions, cosmological parameters, and HOD parameters. Despite the fact that the mock observations are quite small ($\sim 0.07h^{-3}{\mathrm{Gpc}}^3$) and the training data span a large parameter space (6 cosmological and 6 HOD parameters), the CNN and hybrid CNN can constrain ${\sigma }_8$ and ${\Omega }_m$ to $\sim 3\%$ and $\sim 4\%$, respectively.

Submitted to The Astrophysical Journal

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• Towards an optimal extraction of cosmological parameters from galaxy cluster surveys using convolutional neural networks
• Cosmological constraints from the density gradient weighted correlation function