Skip to main content Accessibility help
×
Home

Neural Network Emulation of Reionization Simulations

  • Claude J. Schmit (a1) (a2) and Jonathan R. Pritchard (a1) (a3)

Abstract

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.

    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Neural Network Emulation of Reionization Simulations
      Available formats
      ×

      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Neural Network Emulation of Reionization Simulations
      Available formats
      ×

      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Neural Network Emulation of Reionization Simulations
      Available formats
      ×

Copyright

References

Hide All
Greig, B. & Mesinger, A., 2015, MNRAS, 449, 4246
Shimabukuro, H. & Semelin, B., 2017, MNRAS, 468, 3869
Mesinger, A. & Furlanetto, S., 2007, ApJ, 669, 663
Schmit, C. J. & Pritchard, J. R. 2017, preprint, arxiv:1708.00011
Gal, Y. 2016, PhD Thesis, University of Cambridge
Mcloone, S. F., Asirvadam, V. S., & Irwin, G. W., 2002, IEEE Int. Conf. Neural Networks, 2, 513
Kern, N. S., Liu, A., Parsons, A. R., Mesinger, A., & Greig, B., 2017, ApJ, 848, 23
Iliev, I. T., Mellema, G., Pen, U. L., Merz, H., Shapiro, P. R., & Alvarez, M. A., 2006, MNRAS, 369, 1625
Semelin, B., Eames, E., Bolgar, F., & Caillat, M., 2017, MNRAS, 472, 4508
MathJax
MathJax is a JavaScript display engine for mathematics. For more information see http://www.mathjax.org.

Keywords

Related content

Powered by UNSILO

Neural Network Emulation of Reionization Simulations

  • Claude J. Schmit (a1) (a2) and Jonathan R. Pritchard (a1) (a3)

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed.