Improving constraints on the reionization parameters using 21-cm bispectrum

• Published in: Journal of cosmology and astroparticle physics 2022-04, Vol.2022 (4), p.45
• Main Authors: Tiwari, Himanshu, Shaw, Abinash Kumar, Majumdar, Suman, Kamran, Mohd, Choudhury, Madhurima
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Bayesian analysis; Bayesian reasoning; Emulators; Ionization; Machine learning; non-gaussianity; Parameters; reionization; Sample variance; Statistical inference
• ISSN: 1475-7516; 1475-7516
• DOI: 10.1088/1475-7516/2022/04/045

Radio interferometric experiments aim to constrain the reionization model parameters by measuring the 21-cm signal statistics, primarily the power spectrum. However the Epoch of Reionization (EoR) 21-cm signal is highly non-Gaussian, and this non-Gaussianity encodes important information about this era. The bispectrum is the lowest order statistic able to capture this inherent non-Gaussianity. Here we are the first to demonstrate that bispectra for large and intermediate length scales and for all unique k -triangle shapes provide tighter constraints on the EoR parameters compared to the power spectrum or the bispectra for a limited number of shapes of k -triangles. We use the Bayesian inference technique to constrain EoR parameters. We have also developed an Artificial Neural Network (ANN) based emulator for the EoR 21-cm power spectrum and bispectrum which we use to remarkably speed up our parameter inference pipeline. Here we have considered the sample variance and the system noise uncertainties corresponding to 1000 hrs of SKA-Low observations for estimating errors in the signal statistics. We find that using all unique k -triangle bispectra improves the constraints on parameters by a factor of 2 – 4 (depending on the stage of reionization) over the constraints that are obtained using power spectrum alone.