Journal article
Deep learning from 21-cm tomography of the cosmic dawn and reionization
Nicolas Gillet, Andrei Mesinger, Bradley Greig, Adrian Liu, Graziano Ucci
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY | OXFORD UNIV PRESS | Published : 2019
DOI: 10.1093/mnras/stz010
Abstract
The 21-cm power spectrum (PS) has been shown to be a powerful discriminant of reionization and cosmic dawn astrophysical parameters. However, the 21-cm tomographic signal is highly non-Gaussian. Therefore there is additional information which is wasted if only the PS is used for parameter recovery. Here we showcase astrophysical parameter recovery directly from 21-cm images, using deep learning with convolutional neural networks (CNN). Using a data base of 2D images taken from 10 000 21-cm light-cones (each generated from different cosmological initial conditions), we show that a CNN is able to recover parameters describing the first galaxies: (i) Tvir , their minimum host halo virial temper..
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Grants
Awarded by European Research Council (ERC) under the European Union
Awarded by Australian Research Council Centre of Excellence
Awarded by NASA through Hubble Fellowship by Space Telescope Science Institute
Awarded by NASA
Funding Acknowledgements
We thank B. Semelin for its useful comments and discussions. This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 638809 -AIDA -PI: Mesinger). The results presented here reflect the authors' views; the ERC is not responsible for their use. Parts of this research were supported by the Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), through project number CE170100013. AL acknowledges support for this work by NASA through Hubble Fellowship grant #HST-HF2-51363.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555. We acknowledge support from INAF under PRIN SKA/CTA FORECaST. We thank contributors to SCIPY,<SUP>9</SUP> MATPLOTLIB,<SUP>10</SUP> PYDOE,<SUP>11</SUP> and the PYTHON programming language.<SUP>12</SUP>