A Hybrid Deep Learning Approach to Cosmological Constraints from Galaxy Redshift Surveys
We present a deep machine learning (ML)–based technique for accurately determining σ8 and Ωm 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 parameter values, 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 (∼0.07 h−3 Gpc3) and the training data span a large parameter space (six cosmological and six HOD parameters), the CNN and hybrid CNN can constrain estimates of σ8 and Ωm to ∼3% and ∼4%, respectively.