The BINGO project X. Cosmological parameter constraints from HI Intensity Mapping lognormal simulations

Abstract

Building on the transformative success of optical redshift surveys, the emerging technique of neutral hydrogen (HI) intensity mapping (IM) offers a novel probe of large-scale structure (LSS) growth and the late-time accelerated expansion of the universe. We present cosmological forecasts for the Baryon Acoustic Oscillations from Integrated Neutral Gas Observations (BINGO), a pioneering HI IM experiment, quantifying its potential to constrain the \textit{Planck}-calibrated $Λ$CDM cosmology and extensions to the $w_0w_a$CDM dark energy model. For BINGO's Phase~1 configuration, we simulate the HI IM signal using a lognormal model and incorporate three dominant systematics: foreground residuals, thermal noise, and beam resolution effects. Using Bayesian inference, we derive joint constraints on six cosmological parameters ($Ω_b h^2$, $Ω_c h^2$, $100θ_s$, $n_s$, $\ln 10^{10} A_s$, and $τ_r$) alongside 60 HI parameters ($b_{\rm HI}^i$, $Ω_{\rm HI}^i b_{\rm HI}^i$) across 30 frequency channels. Our results demonstrate that combining BINGO with the Planck 2018 CMB dataset tightens the confidence regions of cosmological parameters to $\sim$40\% the size of those from Planck alone, significantly improving the precision of parameter estimation. Furthermore, BINGO constrains the redshift evolution of HI density and delivers competitive measurements of the dark energy equation of state parameters ($w_0$, $w_a$). These results demonstrate BINGO's potential to extract significant cosmological information from the HI distribution and provide constraints competitive with current and future cosmological surveys.

Figures (11)

• Figure 1: From top to bottom, each panel shows the pure 21-cm cosmological signal generated with the FLASK code (subsection \ref{['sec.cosmological_signal']}) convolved with a Gaussian beam of $\theta_{\rm FWHM} = 40$ arcmin, a realization of the white noise contribution (subsection \ref{['sec.instrumental']}), the cosmological signal and white noise summed up and the reconstructed map using FastICA.
• Figure 2: Comparison of angular power spectra $S_\ell$ for different simulated components. The black solid line shows the theoretical 21-cm spectrum from UCLCl. Colored lines represent measured spectra from simulations: pure 21-cm signal (blue), the sum of 21-cm and white noise (orange), the reconstructed spectrum after foreground removal (green, biased), and the final debiased spectrum (red) after applying the correction from Eq. \ref{['eq.debiasing']}.
• Figure 3: The blue, green, and red lines represent the relative error $(\hat{S}_\ell - S^{\mathrm{th}}_\ell)/\sigma_\ell$ computed for three different realizations of the BINGO simulations including White Noise and foreground removal. $\sigma_\ell$ is the standard deviation derived from the covariance matrix, as described in Sec. \ref{['sec.covariance_matrix']}. The black line shows the average relative error over 3000 simulations. This plot illustrates that neighboring multipoles are highly correlated. Each panel corresponds to a different frequency channel, as defined in Table \ref{['tab.multipole_selection']}, with the channel number indicated in the panel title.
• Figure 4: Pearson correlation coefficient $\mathcal{C}_{LL^\prime}/\sqrt{\mathcal{C}_{LL}\mathcal{C}_{L^\prime L^\prime}}$ where $\mathcal{C}_{LL^\prime}$ is the covariance matrix. In this plot, we did not include any systematics, so the correlation represents the pure 21-cm signal. There are two overall behaviors: the correlation increases with multipole, and the correlation decreases with redshift.
• Figure 5: Pearson correlation coefficient $\mathcal{C}_{LL\prime}/\sqrt{\mathcal{C}_{LL}\mathcal{C}_{L\prime L\prime}}$ where $\mathcal{C}_{LL\prime}$ is the covariance matrix. White Nose and foreground removal systematic effects included. As in the pure 21-cm case, the correlation decreases with frequency channel. For the latest frequency channels, the correlation decreases for multipoles above $\ell \approx 100$ due to the deacrease of the signal-to-noise ratio. Additionaly for the earliest channels, the correlations remain high as an effect of foreground removal.