Joint power spectrum and voxel intensity distribution forecast on the CO luminosity function with COMAP

TL;DR

This paper introduces a Bayesian framework combining power spectra and voxel intensity distributions to improve constraints on the CO luminosity function from intensity mapping data, demonstrated with COMAP simulations.

Contribution

It develops a joint analysis method for PS and VID, calibrated with simulations, enhancing the accuracy of CO luminosity function constraints over previous separate approaches.

Findings

VID is more sensitive at small and large luminosities.

Joint analysis outperforms individual PS or VID constraints.

Uncertainty reduction of 58% (PS) and 30% (VID) when combined.

Abstract

We develop a framework for joint constraints on the CO luminosity function based on power spectra (PS) and voxel intensity distributions (VID), and apply this to simulations of COMAP, a CO intensity mapping experiment. This Bayesian framework is based on a Markov chain Monte Carlo (MCMC) sampler coupled to a Gaussian likelihood with a joint PS + VID covariance matrix computed from a large number of fiducial simulations, and re-calibrated with a small number of simulations per MCMC step. The simulations are based on dark matter halos from fast peak patch simulations combined with the $L_\text{CO}(M_\text{halo})$ model of Li et al. (2016). We find that the relative power to constrain the CO luminosity function depends on the luminosity range of interest. In particular, the VID is more sensitive at both small and large luminosities, while the PS is more sensitive at intermediate luminosities. The joint analysis is superior to using either observable separately. When averaging over CO luminosities ranging between $L_\text{CO} = 10^4-10^7L_\odot$, and over 10 cosmological realizations of COMAP Phase 2, the uncertainties (in dex) are larger by 58 % and 30 % for the PS and VID, respectively, when compared to the joint analysis (PS + VID). This method is generally applicable to any other random field, with a complicated likelihood, as long a fast simulation procedure is available.