Skip to main content Accessibility help
×
Home

Foreground Subtraction and Signal reconstruction in redshifted 21cm Global Signal Experiments using Artificial Neural Networks

  • Madhurima Choudhury (a1) and Abhirup Datta (a1)

Abstract

Observations of HI 21cm transition line is a promising probe into the Dark Ages and Epoch-of-Reionization. Detection of this redshifted 21cm signal is one of the key science goal for several upcoming low-frequency radio telescopes like HERA, SKA and DARE. Other global signal experiments include EDGES, LEDA, BIGHORNS, SCI-HI, SARAS. One of the major challenges for the detection of this signal is the accuracy of the foreground source removal. Several novel techniques have been explored already to remove bright foregrounds from both interferometric as well as total power experiments. Here, we present preliminary results from our investigation on application of ANN to detect 21cm global signal amidst bright galactic foreground. Following the formalism of representing the global 21cm signal by ’tanh’ model, this study finds that the global 21cm signal parameters can be accurately determined even in the presence of bright foregrounds represented by 3rd order log-polynomial or higher.

    • 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.

      Foreground Subtraction and Signal reconstruction in redshifted 21cm Global Signal Experiments using Artificial Neural Networks
      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.

      Foreground Subtraction and Signal reconstruction in redshifted 21cm Global Signal Experiments using Artificial Neural Networks
      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.

      Foreground Subtraction and Signal reconstruction in redshifted 21cm Global Signal Experiments using Artificial Neural Networks
      Available formats
      ×

Copyright

References

Hide All
Furlanetto, S. R., Oh, S. P. & Briggs, F. H. Cosmology at low frequencies: The 21 cm transition and the high-redshift Universe. Phys. Rep., 433: 181301, October 2006.
Barkana, R. & Loeb, A. A Method for Separating the Physics from the Astrophysics of High-Redshift 21 Centimeter Fluctuations. ApJL, 624: L65L68, May 2005.
Harker, G. J. A. Selection between foreground models for global 21-cm experiments. MNRAS, 449: L21L25, April 2015.
Pritchard, J., Ichiki, K., Mesinger, A., Metcalf, R. B., Pourtsidou, A., Santos, M., Abdalla, F. B., Chang, T. C., Chen, X., Weller, J. & Zaroubi, S. Cosmology from EoR/Cosmic Dawn with the SKA. Advancing Astrophysics with the Square Kilometre Array (AASKA14), art. 12, April 2015.
Shimabukuro, H. & Semelin, B. Analysing the 21 cm signal from the epoch of reionization with artificial neural networks. MNRAS, 468: 38693877, July 2017.
Mirocha, J., Harker, G. J. A. & Burns, J. O. Interpreting the Global 21-cm Signal from High Redshifts. II. Parameter Estimation for Models of Galaxy Formation. ApJ, 813: 11, November 2015.
Kingma, D. P. & Ba, J. Adam: A Method for Stochastic Optimization. ArXiv e-prints, December 2014.
MathJax
MathJax is a JavaScript display engine for mathematics. For more information see http://www.mathjax.org.

Keywords

Related content

Powered by UNSILO

Foreground Subtraction and Signal reconstruction in redshifted 21cm Global Signal Experiments using Artificial Neural Networks

  • Madhurima Choudhury (a1) and Abhirup Datta (a1)

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.