Skewing the CMBxLSS: a fast method for bispectrum analysis

Upcoming cosmic microwave background (CMB) lensing measurements and tomographic galaxy surveys are expected to provide us with high-precision data sets in the coming years, thus paving the way for fruitful cross-correlation analyses. In this paper we study the information content of the weighted skew-spectrum, a nearly-optimal estimator of the angular bispectrum amplitude, as a means to extract non-Gaussian information on both bias and cosmological parameters from the bispectra of galaxies cross-correlated with CMB lensing, while gaining significantly on speed. Our results show that for the combination of the Planck satellite and the Dark Energy Spectroscopic Instrument (DESI), the difference in the constraints on bias and cosmological parameters from the skew-spectrum and the bispectrum is at most 17%. We further compare and find agreement between our theoretical skew-spectra and those estimated from N-body simulations, for which it is important to include gravitational non-linearities beyond perturbation theory and the post-Born effect for CMB lensing. We define an algorithm to apply the skew-spectrum estimator to the data and, as a preliminary step, we use the skew-spectra to constrain bias parameters and the amplitude of shot noise from the simulations through a Markov chain Monte Carlo likelihood analysis, finding that it may be possible to reach percent-level estimates for the linear bias parameter b(1).

Automated summary

This paper explores the information content of the weighted skew-spectrum in extracting non-Gaussian information from bispectra of galaxies cross-correlated with CMB lensing, showing a potential 17% difference in constraints on bias and cosmological parameters compared to traditional bispectrum analysis. Theoretical skew-spectra are compared with those from N-body simulations, with emphasis on including gravitational non-linearities beyond perturbation theory. An algorithm for applying the skew-spectrum estimator is defined, providing percent-level estimates for the linear bias parameter b(1) through simulations.