2019/09/23 by Michelle Ntampaka, Daniel J. Eisenstein, Sihan Yuan +1
Physics and Astronomy · #Astronomy and Astrophysical Research #Convolutional neural network #Deep learning #Galaxies: Formation, Evolution, Phenomena #Galaxy #Galaxy formation and evolution #Gamma-ray bursts and supernovae #Halo #Parameter space #Redshift #Suite #astro-ph.CO
paper · pdf · doi:10.3847/1538-4357/ab5f5e
Submitted to The Astrophysical Journal
arxiv created 2019/09/23 · openalex created_date 2019/10/03 · openalex publication_date 2020/02/01 · arxiv updated 2020/02/12 · openalex updated_date 2026/08/05
Abstract We present a deep machine learning (ML)–based technique for accurately determining σ 8 and Ω 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 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 Gpc 3 ) and the training data span a large parameter space (six cosmological and six HOD parameters), the CNN and hybrid CNN can constrain estimates of σ 8 and Ω m to ∼3% and ∼4%, respectively.