{
  "id": "1908.10590",
  "version": "v1",
  "published": "2019-08-28T07:57:59.000Z",
  "updated": "2019-08-28T07:57:59.000Z",
  "title": "Cosmological parameter estimation from large-scale structure deep learning",
  "authors": [
    "Shuyang Pan",
    "Miaoxin Liu",
    "Jaime Forero-Romero",
    "Cristiano G. Sabiu",
    "Zhigang Li",
    "Haitao Miao",
    "Xiao-Dong Li"
  ],
  "comment": "15 pages, 10 figures",
  "categories": [
    "astro-ph.CO",
    "gr-qc"
  ],
  "abstract": "We propose a light-weight deep convolutional neural network to estimate the cosmological parameters from simulated 3-dimensional dark matter distributions with high accuracy. The training set is based on 465 realizations of a cubic box size of $256\\ h^{-1}\\ \\rm Mpc$ on a side, sampled with $128^3$ particles interpolated over a cubic grid of $128^3$ voxels. These volumes have cosmological parameters varying within the flat $\\Lambda$CDM parameter space of $0.16 \\leq \\Omega_m \\leq 0.46$ and $2.0 \\leq 10^9 A_s \\leq 2.3$. The neural network takes as an input cubes with $32^3$ voxels and has three convolution layers, three dense layers, together with some batch normalization and pooling layers. We test the error-tolerance abilities of the neural network, including the robustness against smoothing, masking, random noise, global variation, rotation, reflection and simulation resolution. In the final predictions from the network we find a $2.5\\%$ bias on the primordial amplitude $\\sigma_8$ that can not easily be resolved by continued training. We correct this bias to obtain unprecedented accuracy in the cosmological parameter estimation with statistical uncertainties of $\\delta \\Omega_m$=0.0015 and $\\delta \\sigma_8$=0.0029. The uncertainty on $\\Omega_m$ is 6 (and 4) times smaller than the Planck (and Planck+external) constraints presented in \\cite{ade2016planck}.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-08-28T07:57:59.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "cosmological parameter estimation",
      "large-scale structure deep learning",
      "light-weight deep convolutional neural network"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 15,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}