Bayesian Multi-line Intensity Mapping

Abstract

Line intensity mapping (LIM) has emerged as a promising tool for probing the 3D large-scale structure through the aggregate emission of spectral lines. The presence of interloper lines poses a crucial challenge in extracting the signal from the target line in LIM. In this work, we introduce a novel method for LIM analysis that simultaneously extracts line signals from multiple spectral lines, utilizing the covariance of native LIM data elements defined in the spectral--angular space. We leverage correlated information from different lines to perform joint inference on all lines simultaneously, employing a Bayesian analysis framework. We present the formalism, demonstrate our technique with a mock survey setup resembling the SPHEREx deep field observation, and consider four spectral lines within the SPHEREx spectral coverage in the near infrared: H$\hat{I}\pm$, $[$\ion{O}{3}$]$, H${\hat{I}}^2$, and $[$\ion{O}{2}$]$. We demonstrate that our method can extract the power spectrum of all four lines at the $\gtrsim 10\ddot{I}ƒ$ level at $z<2$. For the brightest line, H$\hat{I}\pm$, the $10\ddot{I}ƒ$ sensitivity can be achieved out to $z\sim 3$. Our technique offers a flexible framework for LIM analysis, enabling simultaneous inference of signals from multiple line emissions while accommodating diverse modeling constraints and parameterizations.

27 pages, 18 figures, accepted by ApJ