Skip to main content Accessibility help
×
Home

Reionization Models Classifier using 21cm Map Deep Learning

  • Sultan Hassan (a1) (a2), Adrian Liu (a3), Saul Kohn (a2), James E. Aguirre (a2), Paul La Plante (a2) and Adam Lidz (a2)...

Abstract

Next-generation 21cm observations will enable imaging of reionization on very large scales. These images will contain more astrophysical and cosmological information than the power spectrum, and hence providing an alternative way to constrain the contribution of different reionizing sources populations to cosmic reionization. Using Convolutional Neural Networks, we present a simple network architecture that is sufficient to discriminate between Galaxy-dominated versus AGN-dominated models, even in the presence of simulated noise from different experiments such as the HERA and SKA.

    • 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.

      Reionization Models Classifier using 21cm Map Deep Learning
      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.

      Reionization Models Classifier using 21cm Map Deep Learning
      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.

      Reionization Models Classifier using 21cm Map Deep Learning
      Available formats
      ×

Copyright

References

Hide All
Becker, G. D. & Bolton, J. S., 2013, MNRAS, 436, 1023
Becker, G. D., et al. 2015, MNRAS, 447, 3402
Bond, J. R., Cole, S., Efstathiou, G., & Kaiser, N., 1991, APJ, 379, 440
Davé, R., et al. 2013, MNRAS, 434, 2645
Fan, X., Carilli, C. L., & Keating, B., 2006, Annual Review of Astronomy and Astrophysics, 44, 415
Ferrarese, L., 2002, APJ, 578, 90
Finlator, K., et al. 2015, MNRAS, 447, 2526
Giallongo, E., et al. 2015, A&A, 578, A83
Hassan, S., et al. 2016, MNRAS, 457, 1550
Hassan, S., et al. 2017, MNRAS, 468, 122
Hassan, S., et al. 2018, MNRAS, 473, 227
Hopkins, P. F., Richards, G. T., & Hernquist, L., 2007, APJ, 654, 731
Planck intermediate results. XLVII, Adam, R., Aghanim, N., et al. 2016, arXiv:1605.03507
Pober, J. C., Liu, A. et al. 2014, APJ, 782, 66.
Press, W. H. & Schechter, P., 1974, APJ, 187, 425.
Santos, M. G., et al. 2010, MNRAS, 406, 2421
Shapiro, P. R. & Giroux, M. L., 1987, APJ, 321, 107
Tremaine, S., et al. 2002, APJ, 574, 740
Worseck, G., et al. 2016, APJ, 825, 144
MathJax
MathJax is a JavaScript display engine for mathematics. For more information see http://www.mathjax.org.

Keywords

Related content

Powered by UNSILO

Reionization Models Classifier using 21cm Map Deep Learning

  • Sultan Hassan (a1) (a2), Adrian Liu (a3), Saul Kohn (a2), James E. Aguirre (a2), Paul La Plante (a2) and Adam Lidz (a2)...

Metrics

Altmetric attention score

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.