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

University of Melbourne Researchers

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>