Updated neutrino mass constraints from galaxy clustering and CMB lensing-galaxy cross-correlation measurements
Document Type
Article
Publication Date
11-2022
Department
Department of Physics
Abstract
We revisit cosmological constraints on the sum of the neutrino masses Σmν from a combination of full-shape BOSS galaxy clustering [P(k)] data and measurements of the cross-correlation between Planck Cosmic Microwave Background (CMB) lensing convergence and BOSS galaxy overdensity maps [Cℓκg], using a simple but theoretically motivated model for the scale-dependent galaxy bias in auto- and cross-correlation measurements. We improve upon earlier related work in several respects, particularly through a more accurate treatment of the correlation and covariance between P(k) and Cℓκg measurements. When combining these measurements with Planck CMB data, we find a 95% confidence level upper limit of Σmν < 0.14eV, while slightly weaker limits are obtained when including small-scale ACTPol CMB data, in agreement with our expectations. We confirm earlier findings that (once combined with CMB data) the full-shape information content is comparable to the geometrical information content in the reconstructed BAO peaks given the precision of current galaxy clustering data, discuss the physical significance of our inferred bias and shot noise parameters, and perform a number of robustness tests on our underlying model. While the inclusion of Cℓκg measurements does not currently appear to lead to substantial improvements in the resulting Σmν constraints, we expect the converse to be true for near-future galaxy clustering measurements, whose shape information content will eventually supersede the geometrical one.
Publication Title
Journal of High Energy Astrophysics
Recommended Citation
Tanseri, I.,
Hagstotz, S.,
Vagnozzi, S.,
Giusarma, E.,
&
Freese, K.
(2022).
Updated neutrino mass constraints from galaxy clustering and CMB lensing-galaxy cross-correlation measurements.
Journal of High Energy Astrophysics,
36, 1-26.
http://doi.org/10.1016/j.jheap.2022.07.002
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/16317