The Marked Power Spectrum as a Practical Bispectrum Measure for Galaxy Redshift Surveys

TL;DR

This paper explores the marked power spectrum as a practical higher-order statistic for galaxy redshift surveys, demonstrating its ability to break parameter degeneracies and its robustness to survey geometry effects.

Contribution

It introduces a restructuring of the marked power spectrum to isolate higher-order information and assesses its modeling, covariance, and cosmology dependence for improved cosmological inference.

Findings

Marked power spectrum effectively breaks parameter degeneracies.

Survey geometry effects are manageable with standard treatment.

Cosmology dependence is smooth, enabling interpolation-based inference.

Abstract

Modern datasets have the precision necessary to uncover new information by including higher-order, non-Gaussian information into cosmological inference. The marked power spectrum offers access to such information while preserving the structure of two-point correlators. This approach to higher-order statistics has the advantage that many modeling questions can directly benefit from progress already made in standard cosmological analyses using the power spectrum and correlation function, while increasing the data vector size negligibly and retaining much of the degeneracy-breaking power of the bispectrum. In this work, we first restructure the marked power spectrum to isolate its higher-order information and demonstrate its ability to break parameter degeneracies. We then investigate the effect of survey geometry on the marked power spectrum and find that a treatment similar to that of the power spectrum is sufficient. Additionally, we investigate the perturbative modeling and covariance structure of the marked power spectrum, shedding light on its degeneracy breaking power and cross-covariance with the power spectrum. Finally, we demonstrate that the cosmology dependence of the marked power spectrum is smooth, indicating that cosmological inference is possible by modeling the cosmology dependence through interpolation rather than analytical modeling.