Multitracing Anisotropic Non-Gaussianity with Galaxy Shapes

TL;DR

The paper addresses the challenge of probing anisotropic primordial non-Gaussianity through intrinsic galaxy alignments. It proposes a multi-tracer strategy using two distinct shape estimators, combined with blue galaxies and clustering, to beat cosmic variance and isolate the $A_2$-dependent imprint on alignments. A Fisher forecast for LSST and Euclid shows that $\sigma(A_2)$ can reach about $50$, representing a ~40% improvement over current CMB constraints and a ~4–5× gain over single-tracer alignment analyses. The work demonstrates the potential of intrinsic alignments as a cosmological probe of inflation and highlights the conditions under which multi-tracer gains are maximized, including noise correlations and tomographic extensions.

Abstract

Correlations between intrinsic galaxy shapes on large-scales arise due to the effect of the tidal field of the large-scale structure. Anisotropic primordial non-Gaussianity induces a distinct scale-dependent imprint in these tidal alignments on large scales. Motivated by the observational finding that the alignment strength of luminous red galaxies depends on how galaxy shapes are measured, we study the use of two different shape estimators as a multi-tracer probe of intrinsic alignments. We show, by means of a Fisher analysis, that this technique promises a significant improvement on anisotropic non-Gaussianity constraints over a single-tracer method. For future weak lensing surveys, the uncertainty in the anisotropic non-Gaussianity parameter, $A_2$, is forecast to be $σ(A_2)\approx 50$, $\sim 40\%$ smaller than currently available constraints from the bispectrum of the Cosmic Microwave Background. This corresponds to an improvement of a factor of $4-5$ over the uncertainty from a single-tracer analysis.

Figures (4)

• Figure 1: Fraction of red galaxies in LSST (black) and Euclid (red) as a function of redshift. The legend indicates the corresponding magnitude limits of the surveys.
• Figure 2: Forecasted constraints on $A_2$ from LSST (solid) and Euclid (dashed) as a function of the correlation coefficient of the noise of the first and second alignment tracer, $r_n^{(\gamma)}$. The top panel represents constraints from a single red tracer (red), and the new results with the addition of a second alignment tracer (black). The middle panel includes blue galaxy correlations. The bottom panel includes the blue position-red shape correlations. The blue dashed line labeled "SM16" refers to the value of $r_n^{(\gamma)}$ determined observationally by Singh16.
• Figure 3: Forecasted uncertainty in $\tilde{b}_I^{\rm NG}A_2$ from LSST as a function of maximum (top) or minimum (bottom) multipole probed. All cases include blue galaxies and their cross-correlations with the red sample. For the top panel, we fix the minimum multipole probed at $l=2$. Constraints improve with higher multipole, but the improvement as we approach nonlinear scales becomes marginal. For the bottom panel, we fix the maximum multipole probed to $l_{\rm max}=600$. The constraining power increases for smaller $l_{\rm min}$.
• Figure 4: Forecasted constraints on the $(A_0,\tilde{b}_I^{\rm NG}A_2)$ plane for Euclid. The top left panel represents the constraints from a single red tracer. More tracers are incorporated to the right: a second shape tracer (top right panel), blue galaxies (bottom left panel) and cross-correlations of red and blue galaxies (bottom right panel). The black, blue and red ellipses represent the ${1,2,3}\sigma$ contours, respectively. In these figures, we assume a level of correlated noise consistent with Singh16.