Hefty enhancement of cosmological constraints from the DES Y1 data using a hybrid effective field theory approach to galaxy bias

Journal article

We present a re-analysis of cosmic shear and galaxy clustering from first-year Dark Energy Survey data (DES Y1), making use of a Hybrid Effective Field Theory (HEFT) approach to model the galaxy-matter relation on weakly non-linear scales, initially proposed in [1]. This allows us to explore the enhancement in cosmological constraining power enabled by extending the galaxy clustering scale range typically used in projected large-scale structure analyses. Our analysis is based on a recomputed harmonic-space data vector and covariance matrix, carefully accounting for all sources of mode-coupling, non-Gaussianity and shot noise, which allows us to provide robust goodness-of-fit measures. We use the AbacusSummit suite of simulations to build an emulator for the HEFT model predictions. We find that this model can explain the galaxy clustering and shear data up to wavenumbers k_max∼ 0.6 Mpc^-1. We constrain (S₈,Ω_m) = (0.786± 0.020,0.273^+0.030_-0.036) at the fiducial k_max∼ 0.3 Mpc^-1, improving to (S₈,Ω_m) = (0.786^+0.015_-0.018,0.266^+0.024_-0.027) at k_max∼ 0.5 Mpc^-1. This represents a ∼10% and ∼35% improvement on the constraints derived respectively on both parameters using a linear bias relation on a reduced scale range (k_max≲0.15 Mpc^-1), in spite of the 15 additional parameters involved in the HEFT model. We investigate whether HEFT can be used to constrain the Hubble parameter and find H₀= 70.7_-3.5^+3.0 km s^-1 Mpc^-1. Our constraints are investigative and subject to certain caveats discussed in the text.

Authors: Hadzhiyska, B; García-García, C; Alonso, D; Nicola, A; Slosar, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IOP Publishing
• Journal: Journal of Cosmology and Astroparticle Physics
• Volume: 2021
• Issue: 09
• Article number: 020
• Publication date: 2021-09-15
• Acceptance date: 2021-08-02
• DOI: 10.1088/1475-7516/2021/09/020
• EISSN: 1475-7516
• Language: English
• Keywords: FFR