%0 Generic %1 tiwari2021improving %A Tiwari, Himanshu %A Shaw, Abinash Kumar %A Majumdar, Suman %A Kamran, Mohd %A Choudhury, Madhurima %D 2021 %K library %T Improving constraints on the reionization parameters using 21-cm bispectrum %U http://arxiv.org/abs/2108.07279 %X 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.

@misc{tiwari2021improving, abstract = {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.}, added-at = {2021-08-18T06:16:12.000+0200}, author = {Tiwari, Himanshu and Shaw, Abinash Kumar and Majumdar, Suman and Kamran, Mohd and Choudhury, Madhurima}, biburl = {https://www.bibsonomy.org/bibtex/2c8ada532af33f71b6f99aa06529d2d06/gpkulkarni}, description = {Improving constraints on the reionization parameters using 21-cm bispectrum}, interhash = {8d12244752c423f5ff63e2c3cb369786}, intrahash = {c8ada532af33f71b6f99aa06529d2d06}, keywords = {library}, note = {cite arxiv:2108.07279Comment: 13 pages, 9 figures, submitted to MNRAS, comments are welcome}, timestamp = {2021-08-18T06:16:12.000+0200}, title = {Improving constraints on the reionization parameters using 21-cm bispectrum}, url = {http://arxiv.org/abs/2108.07279}, year = 2021 }