Extracting intrinsic alignments in the Dark Energy Survey's year 1 data, using the self-calibration method and LSST-DESC tools

TL;DR

This paper implements a self-calibration method for intrinsic alignments in galaxy surveys, applying it to DES Year 1 data with LSST-DESC tools, revealing potential signals at higher redshifts and discussing photo-z estimation challenges.

Contribution

It introduces an extension of the LSST-DESC pipeline for self-calibration of intrinsic alignments and demonstrates its application to real survey data.

Findings

Indications of non-zero intrinsic alignments at higher redshifts.

Uncertainty in lower redshift bins due to photo-z estimation issues.

Impact of redshift probability distribution methods on alignment detection.

Abstract

We present the implementation of a Self-Calibration of Intrinsic Alignments of galaxies as an extension to the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC)'s weak lensing 3x2pt pipeline (TXPipe). As a demonstration, we have run this pipeline on the Dark Energy Survey (DES) year one data set. We find indications of a non-zero intrinsic alignment signal in the higher redshift bins, while in the lower bins our results look more uncertain. We believe this is caused by known issues with the individual galaxies photo-z estimation. This effect is particularly harmful for the self-calibration method, since it has high requirements for reliable estimation of the photo-$z$s, and the need for individual galaxy point estimates and tomographic binning to match. We show how different methods of recreating the redshift probability distribution can affect the detection of intrinsic alignment.