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Dark Energy Survey Year 1 Results: Constraints on Intrinsic Alignments and their Colour Dependence from Galaxy Clustering and Weak Lensing

Published 16 Nov 2018 in astro-ph.CO | (1811.06989v2)

Abstract: We perform a joint analysis of intrinsic alignments and cosmology using tomographic weak lensing, galaxy clustering and galaxy-galaxy lensing measurements from Year 1 (Y1) of the Dark Energy Survey. We define early- and late-type subsamples, which are found to pass a series of systematics tests, including for spurious photometric redshift error and point spread function correlations. We analyse these split data alongside the fiducial mixed Y1 sample using a range of intrinsic alignment models. In a fiducial Nonlinear Alignment Model (NLA) analysis, assuming a flat \lcdm~cosmology, we find a significant difference in intrinsic alignment amplitude, with early-type galaxies favouring $A_\mathrm{IA} = 2.38{+0.32}_{-0.31}$ and late-type galaxies consistent with no intrinsic alignments at $0.05{+0.10}_{-0.09}$. We find weak evidence of a diminishing alignment amplitude at higher redshifts in the early-type sample. The analysis is repeated using a number of extended model spaces, including a physically motivated model that includes both tidal torquing and tidal alignment mechanisms. In multiprobe likelihood chains in which cosmology, intrinsic alignments in both galaxy samples and all other relevant systematics are varied simultaneously, we find the tidal alignment and tidal torquing parts of the intrinsic alignment signal have amplitudes $A_1 = 2.66 {+0.67}_{-0.66}$, $A_2=-2.94{+1.94}_{-1.83}$, respectively, for early-type galaxies and $A_1 = 0.62 {+0.41}_{-0.41}$, $A_2 = -2.26{+1.30}_{-1.16}$ for late-type galaxies. In the full (mixed) Y1 sample the best constraints are $A_1 = 0.70 {+0.41}_{-0.38}$, $A_2 = -1.36 {+1.08}_{-1.41}$. For all galaxy splits and IA models considered, we report cosmological parameter constraints that are consistent with the results of Troxel et al. (2017) and Dark Energy Survey Collaboration (2017).

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