Cosmology with the Galaxy Bispectrum Multipoles: Optimal Estimation and Application to BOSS Data
Abstract
We present a framework for self-consistent cosmological analyses of the full-shape anisotropic bispectrum, including the quadrupole and hexadecapole moments. This features a novel window-free algorithm for extracting the latter quantities from data, derived using a maximum-likelihood prescription. Furthermore, we introduce a theoretical model for the bispectrum multipoles (which does not introduce new free parameters), and test both aspects of the pipeline on several high-fidelity mocks, including the PT Challenge suite of gigantic cumulative volume. This establishes that the systematic error is significantly below the statistical threshold, both for the measurement and modeling. As a realistic example, we extract the large-scale bispectrum multipoles from BOSS DR12 and analyze them in combination with the power spectrum data. Assuming a minimal CDM model, with a BBN prior on the baryon density and a \textit{Planck} prior on , we can extract the remaining cosmological parameters directly from the clustering data. The inclusion of the unwindowed higher-order large-scale bispectrum multipoles is found to moderately improve one-dimensional cosmological parameter posteriors (at the level), though these multipoles are detected only in three out of four BOSS data segments at . Combining information from the power spectrum and bispectrum multipoles, the real space power spectrum, and the post-reconstructed BAO data, we find , and (the tightest yet found in perturbative full-shape analyses). Our estimate of the growth parameter agrees with both weak lensing and CMB results.
Cite
@article{arxiv.2302.04414,
title = {Cosmology with the Galaxy Bispectrum Multipoles: Optimal Estimation and Application to BOSS Data},
author = {Mikhail M. Ivanov and Oliver H. E. Philcox and Giovanni Cabass and Takahiro Nishimichi and Marko Simonović and Matias Zaldarriaga},
journal= {arXiv preprint arXiv:2302.04414},
year = {2023}
}
Comments
40 pages, 8 figures, 6 tables; estimators and data are publicly available at https://github.com/oliverphilcox/Spectra-Without-Windows