Learning Intrinsic Alignments from Local Galaxy Environments

TL;DR

This paper introduces DELTA, a deep learning model using graph neural networks to accurately extract intrinsic galaxy alignments from observational data, improving understanding of galaxy shapes and their local environments without relying on simulations.

Contribution

DELTA is a novel deep learning approach that flexibly learns galaxy intrinsic alignments directly from observational data using an equivariant graph neural network, bypassing traditional parametric models.

Findings

DELTA accurately reconstructs pure IA signals from noisy mock catalogs.

The model visualizes IA patterns in galaxy environments.

Insights into tidal physics are gained through deep learning interpretation.

Abstract

We present DELTA (Data-Empiric Learned Tidal Alignments), a deep learning model that isolates galaxy intrinsic alignments (IAs) from weak lensing distortions using only observational data. The model uses an Equivariant Graph Neural Network backbone suitable for capturing information from the local galaxy environment, in conjunction with a probabilistic orientation output. Unlike parametric models, DELTA flexibly learns the relationship between galaxy shapes and their local environments, without assuming an explicit IA form or relying on simulations. When applied to mock catalogs with realistic noisy IAs injected, it accurately reconstructs the noise-free, pure IA signal. Mapping these alignments provides a direct visualization of IA patterns in the mock catalogs. Combining DELTA with deep learning interpretation techniques provides further insights into the physics driving tidal relationships between galaxies. This new approach to understanding and controlling IAs is suitable for application to joint photometric and spectroscopic surveys such as the combination of upcoming Euclid, Rubin, and DESI datasets.