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Neural Network Emulation of Reionization Simulations

Published online by Cambridge University Press:  08 May 2018

Claude J. Schmit Open the ORCID record for Claude J. Schmit [Opens in a new window]  and
Jonathan R. Pritchard Open the ORCID record for Jonathan R. Pritchard [Opens in a new window]
Claude J. Schmit
Affiliation:
Blackett Laboratory, Imperial College, London, SW7 2AZ, UK email: [email protected]
Jonathan R. Pritchard
Affiliation:
Blackett Laboratory, Imperial College, London, SW7 2AZ, UK email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

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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. One of the major challenges for these experiments will be dealing with enormous incoming data volumes. Machine learning is key to increasing our data analysis efficiency. We consider the use of an artificial neural network to emulate 21cmFAST simulations and use it in a Bayesian parameter inference study. We then compare the network predictions to a direct evaluation of the EoR simulations and analyse the dependence of the results on the training set size. We find that the use of a training set of size 100 samples can recover the error contours of a full scale MCMC analysis which evaluates the model at each step.

Keywords

cosmology: reionization - methods: numerical - methods: statistical
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S333: Peering towards Cosmic Dawn , October 2017 , pp. 43 - 46
Copyright
Copyright © International Astronomical Union 2018 

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