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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 α , [O iii ], H β , and [O ii ]. We demonstrate that our method can extract the power spectrum of all four lines at the ≳10 σ level at z < 2. For the brightest line, H α , the 10 σ sensitivity can be achieved out to z ∼ 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.

The dipole moment in the angular distribution of the cosmic microwave background (CMB) is thought to originate from the doppler effect and our motion relative to the CMB frame. Observations of large-scale structure (LSS) should show a related “kinematic dipole” and help test the kinematic origin of the CMB dipole. Intriguingly, many previous LSS dipole studies suggest discrepancies with the expectations from the CMB. Here, we reassess the apparent inconsistency between the CMB measurements and dipole estimates from the NVSS catalog of radio sources. We find that it is important to account for the shot noise and clustering of the NVSS sources, as well as kinematic contributions, in determining the expected dipole signal. We use the clustering redshift method and a cross-matching technique to refine estimates of the clustering term. We then derive a probability distribution for the expected NVSS dipole in a standard ΛCDM cosmological model including all (i.e., kinematic, shot noise, and clustering) dipole components. Our model agrees with most of the previous NVSS dipole measurements in the literature at better than ≲2 σ . We conclude that the NVSS dipole is consistent with a kinematic origin for the CMB dipole within ΛCDM.

We present a data-driven technique to analyze multifrequency images from upcoming cosmological surveys mapping large sky area. Using full information from the data at the two-point level, our method can simultaneously constrain the large-scale structure (LSS), the spectra and redshift distribution of emitting sources, and the noise in the observed data without any prior assumptions beyond the homogeneity and isotropy of cosmological perturbations. In particular, the method does not rely on source detection or photometric or spectroscopic redshift estimates. Here, we present the formalism and demonstrate our technique with a mock observation from nine optical and near-infrared photometric bands. Our method can recover the input signal and noise without bias, and quantify the uncertainty on the constraints. Our technique provides a flexible framework to analyze the LSS observation traced by different types of sources, which has potential for wide application to current or future cosmological data sets such as SPHEREx, Rubin Observatory, Euclid, or the Nancy Grace Roman Space Telescope.