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Interpretation of 21 cm Auto Power Spectrum Measurement at $z\sim 1$ by the Canadian Hydrogen Intensity Mapping Experiment

CHIME Collaboration, Mandana Amiri, Kevin Bandura, Arnab Chakraborty, Zhuo Yu Brian Chu, Matt Dobbs, Simon Foreman, Liam Gray, Mark Halpern, Gary Hinshaw, Albin Joseph, Nolan Kruger, Joshua MacEachern, Kiyoshi W. Masui, Juan Mena-Parra, Laura Newburgh, Tristan Pinsonneault-Marotte, Alex Reda, Shabbir Shaikh, Seth R. Siegel, Yukari Uchibori, Keith Vanderlinde, Haochen Wang, Dallas Wulf

Abstract

Observations with the Canadian Hydrogen Intensity Mapping Experiment (CHIME) have been used to measure the 21 cm intensity mapping auto power spectrum, at $z\sim 1$, over a frequency range from 608.2 MHz to 707.8 MHz at wavenumbers $0.4~h~{\rm Mpc}^{-1} \lesssim k \lesssim 1.5~h~{\rm Mpc}^{-1}$. In this paper, we present the results of two different approaches to interpreting this measurement. In the first approach, we use a parametric power spectrum model to constrain an amplitude parameter, defined as $\mathcal{A}^2_{\rm HI} \equiv 10^6 Ω_{\rm HI}^2(b^2_{\rm HI}+\langle f μ^2\rangle)^2$, where $Ω_{\rm HI}$ is the cosmological density parameter for atomic hydrogen ($\rm HI$), $b_{\rm HI}$ is the linear bias for $\rm HI$, and $\langle f μ^2\rangle$ incorporates the dominant large-scale impact of redshift-space distortions on the angle-averaged power spectrum. Imposing an additional prior on either $Ω_{\rm HI}$ or $b_{\rm HI}$, based on values in the literature, allows us to break the pairwise degeneracy between those two parameters. In the second approach, we compare CHIME's measurement with predictions for the power spectrum of $\rm HI$ from the IllustrisTNG simulations, finding that the measurement disagrees with the TNG100 run at $3.1σ$ and the TNG300 run at $4.0σ$. This disagreement is most likely attributable to the strength of nonlinear redshift-space clustering of $\rm HI$ in the simulations, rather than the total abundance of $\rm HI$, and invites further investigation of the physical processes in the simulations that determine the behavior of $\rm HI$ at nonlinear scales. These results exemplify the ability of 21 cm intensity mapping to provide astrophysical information using measurements at nonlinear scales.

Interpretation of 21 cm Auto Power Spectrum Measurement at $z\sim 1$ by the Canadian Hydrogen Intensity Mapping Experiment

Abstract

Observations with the Canadian Hydrogen Intensity Mapping Experiment (CHIME) have been used to measure the 21 cm intensity mapping auto power spectrum, at , over a frequency range from 608.2 MHz to 707.8 MHz at wavenumbers . In this paper, we present the results of two different approaches to interpreting this measurement. In the first approach, we use a parametric power spectrum model to constrain an amplitude parameter, defined as , where is the cosmological density parameter for atomic hydrogen (), is the linear bias for , and incorporates the dominant large-scale impact of redshift-space distortions on the angle-averaged power spectrum. Imposing an additional prior on either or , based on values in the literature, allows us to break the pairwise degeneracy between those two parameters. In the second approach, we compare CHIME's measurement with predictions for the power spectrum of from the IllustrisTNG simulations, finding that the measurement disagrees with the TNG100 run at and the TNG300 run at . This disagreement is most likely attributable to the strength of nonlinear redshift-space clustering of in the simulations, rather than the total abundance of , and invites further investigation of the physical processes in the simulations that determine the behavior of at nonlinear scales. These results exemplify the ability of 21 cm intensity mapping to provide astrophysical information using measurements at nonlinear scales.

Paper Structure

This paper contains 50 sections, 46 equations, 20 figures.

Table of Contents

  1. Introduction
  2. Data and Processing
  3. Modelling
  4. Cosmological 21$\,$cm power spectrum
  5. Mean 21$\,$cm brightness temperature $\bar{T}_{\rm b}(z)$
  6. Linear bias and Kaiser term $\left[ b_{ {\rm HI}}(z) + f(z) \mu^2 \right]$
  7. Matter power spectrum $P_{\rm m}(k, z)$
  8. Finger-of-God damping $D_{{ {\rm HI}}}^{\rm FoG}(k\mu, z; \alpha_{\rm FoG})$
  9. Template-based model evaluation
  10. Simulations
  11. Method
  12. Validation set
  13. Mock observations of IllustrisTNG simulations
  14. Posterior estimation
  15. Method
  16. ...and 35 more sections

Figures (20)

  • Figure 1: Upper panel: Template-based prediction for the observed 21$\,$cm power spectrum, evaluated at a representative point in parameter space, along with the 6 terms that are summed together to produce this prediction. Each term is computed by generating simulated visibilities with a specific input power spectrum, and applying our power spectrum measurement pipeline to these visibilities. Lower panel: Each term is normalized by its value at $k_{\rm min}=0.41\;h\:\mathrm{Mpc}^{-1}$ to highlight differences in shape, arising from whether the term involves the linear or nonlinear matter power spectrum, along with how many factors of the linear ${\rm HI}$ bias and Kaiser factor ($f\mu^2$) are included.
  • Figure 2: Left panels: Dependence of our 21$\,$cm power spectrum model on each parameter: ${\rm HI}$ density parameter $\Omega_{ {\rm HI}}$, linear bias $b_{ {\rm HI}}$, nonlinearity parameter $\alpha_{\rm NL}$, and Finger-of-God damping parameter $\alpha_{\rm FoG}$. Each parameter significantly affects the overall amplitude of the power spectrum at the scales of interest in this work. Right panels: Each power spectrum is divided by a "base" version with $\Omega_{ {\rm HI}}/\Omega_{ {\rm HI}}^{\rm (fid)} = b_{ {\rm HI}}/b_{ {\rm HI}}^{\rm (fid)} = \alpha_{\rm NL} = \alpha_{\rm FoG}=1$, to highlight the effect of each parameter on the power spectrum shape. These shapes indicate that the first two parameters will be highly degenerate in a Bayesian analysis, while the other two will also exhibit degeneracies but at a weaker level.
  • Figure 3: Upper panel: 21$\,$cm power spectra measured from 20 validation simulations, with model parameters drawn from a Latin hypercube in the 4D parameter space. The power spectra span a wide range of normalizations due to the strong dependence of the normalization on each model parameter. Lower panel: Each power spectrum is normalized to its value at $k_{\rm min}=0.41\;h\:\mathrm{Mpc}^{-1}$. This demonstrates the wide range of power spectrum shapes spanned by the simulations. These simulations are used for testing the robustness of the choices in our Bayesian analysis (Section \ref{['sec:posterior_estimation:validation']}) and the accuracy of our model evaluation pipeline (Section \ref{['sec:parameter_constraints:systematics:template_accuracy']}).
  • Figure 4: Posterior distributions of the model parameters obtained from the 21$\,$cm auto–power spectrum fit for the full band, covering the frequency range $608.2–707.8$ MHz ($z_{\mathrm{eff}} = 1.16$). The darker and lighter shaded regions correspond to the 68% and 95% credible intervals, respectively. A strong degeneracy is evident among several parameters, most notably between $\Omega_{ {\rm HI}}$ and $b_{ {\rm HI}}$. The nuisance parameter $\alpha_{\rm FoG}$ is well constrained, reflecting the strong scale-dependent effect of varying $\alpha_{\rm FoG}$ over the $k$ range probed.
  • Figure 5: Same as Figure \ref{['fig:fullband_posteriors_all']}, but with the derived power spectrum amplitude parameter ${\mathcal{A}_{ {\rm HI}}^2}$. Transforming to ${\mathcal{A}_{ {\rm HI}}^2}$ yields a cleaner and better-constrained marginalized posterior on ${\mathcal{A}_{ {\rm HI}}^2}$, while revealing a residual degeneracy between ${\mathcal{A}_{ {\rm HI}}^2}$ and $\alpha_{\rm NL}$.
  • ...and 15 more figures