Emulation of reionization simulations for Bayesian inference of astrophysics parameters using neural networks

@article{Schmit2017EmulationOR,
  title={Emulation of reionization simulations for Bayesian inference of astrophysics parameters using neural networks},
  author={Claude J. Schmit and Jonathan R. Pritchard},
  journal={Monthly Notices of the Royal Astronomical Society},
  year={2017},
  volume={475},
  pages={1213-1223}
}
  • C. Schmit, J. Pritchard
  • Published 31 July 2017
  • Computer Science
  • Monthly Notices of the Royal Astronomical Society
Next generation radio experiments such as LOFAR, HERA and SKA are expected to probe the Epoch of Reionization and claim a first direct detection of the cosmic 21cm signal within the next decade. Data volumes will be enormous and can thus potentially revolutionize our understanding of the early Universe and galaxy formation. However, numerical modelling of the Epoch of Reionization can be prohibitively expensive for Bayesian parameter inference and how to optimally extract information from… 
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