A User's Guide to Extracting Cosmological Information from Line-Intensity Maps

TL;DR

The paper presents a comprehensive framework to extract cosmological information from line-intensity maps (LIM) while marginalizing over astrophysical uncertainties. It develops a detailed LIM power-spectrum model that includes redshift-space distortions and the Alcock-Paczynski effect, and uses a multipole expansion with an analytically derived covariance to optimize information extraction. A reparameterization isolates cosmological information in measurable combinations such as $oldsymbol{oldsymbol{ abla}}$-parameters, and the authors show that including the hexadecapole ($ell=4$) can significantly improve BAO/RSD constraints, with gains up to about 60–75% in favorable cases. They also discuss observational strategies, redshift binning, and extensions to neutrino masses and primordial non-Gaussianity, outlining paths for further improvements through cross-correlations and higher-order statistics.

Abstract

Line-intensity mapping (LIM) provides a promising way to probe cosmology, reionization and galaxy evolution. However, its sensitivity to cosmology and astrophysics at the same time is also a nuisance. Here we develop a comprehensive framework for modelling the LIM power spectrum, which includes redshift space distortions and the Alcock-Paczynski effect. We then identify and isolate degeneracies with astrophysics so that they can be marginalized over. We study the gains of using the multipole expansion of the anisotropic power spectrum, providing an accurate analytic expression for their covariance, and find a 10%-60% increase in the precision of the baryon acoustic oscillation scale measurements when including the hexadecapole in the analysis. We discuss different observational strategies when targeting other cosmological parameters, such as the sum of neutrino masses or primordial non-Gaussianity, finding that fewer and wider bins are typically more optimal. Overall, our formalism facilitates an optimal extraction of cosmological constraints robust to astrophysics.

Figures (6)

• Figure 1: Comparison of the LIM power spectrum multipoles at $z=2.73$ for the fiducial set of parameters (blue) and the cases where each of the parameters of Eq. \ref{['eq:parameters']} is increased by $5\%$ (except for $\vec{\varsigma}$). The top panels show the true power spectrum, while the bottom panels show the smoothed power spectrum as measured by the generalized experiment considered. From left to right, each column corresponds to the monopole, quadrupole and hexadecapole, respectively. The lower part of each panel shows the ratio or difference (when the multipoles cross zero) between the fiducial power spectrum and the ones with the varied parameters. Dashed lines denote negative values. Note the effect of the window function on all multipoles, especially the quadrupole and hexadecapole: both the fiducial power spectra and their parameter dependence are considerably affected by the smoothing of the map.
• Figure 2: Signal-to-noise ratio per $k$ bin of the measured LIM power spectrum for each redshift bin of the generic experiment considered in this work (color coded) including only the monopole (dotted lines), the monopole and the quadrupole (dashed lines) and adding also the hexadecapole (solid lines).
• Figure 3: Correlation matrices of the parameters $\alpha_\perp$, $\alpha_\parallel$ and $\langle T\rangle f\sigma_8$, marginalized over the nuisance parameters, for each redshift bin, calculated for our generalized experiment. The correlations in the lower triangular matrix correspond to the case where the monopole and quadrupole are included. In the upper triangular matrix the hexadecapole is included as well.
• Figure 4: Forecasted 68% confidence-level marginalized 2D constraints for the pair combinations $\alpha_\perp$, $\alpha_\parallel$ and $\langle T\rangle f\sigma_8$, and their corresponding marginalized one-dimensional distributions. We show results using the monopole and quadrupole (blue), and when including the hexadecapole as well (orange).
• Figure 5: Forecasted 68% confidence-level marginalized constraints on $f_{\rm NL}$ (top panels) and on $\sum m_\nu$ (bottom panels) for our general experiment using the monopole, quadrupole and hexadecapole, as function of $\Omega_{\rm field}$ and $N_{\rm pol}N_{\rm feeds}N_{\rm ant}t_{\rm obs}/T_{\rm sys}^2$, a combination of instrumental parameters which determines the measurement sensitivity. We compare results using a single redshift bin (Left panels) or five bins (Right panels). Note the change of scale in the color bars.