Learning Galaxy Intrinsic Alignment Correlations

TL;DR

This paper introduces a deep learning model that efficiently predicts galaxy intrinsic alignment correlation functions and uncertainties from mock catalogs, aiding in the mitigation of IA effects in weak lensing studies.

Contribution

The work presents a novel deep learning approach to emulate galaxy correlation functions and uncertainties, reducing reliance on expensive simulations for IA modeling.

Findings

Model achieves ≤10% accuracy on $\xi(r)$ predictions.

Successfully captures signals of noisier correlations $\omega(r)$ and $\eta(r)$.

Performance limited by data stochasticity, improved with multiple realizations.

Abstract

The intrinsic alignments (IA) of galaxies, regarded as a contaminant in weak lensing analyses, represents the correlation of galaxy shapes due to gravitational tidal interactions and galaxy formation processes. As such, understanding IA is paramount for accurate cosmological inferences from weak lensing surveys; however, one limitation to our understanding and mitigation of IA is expensive simulation-based modeling. In this work, we present a deep learning approach to emulate galaxy position-position ($\xi$), position-orientation ($\omega$), and orientation-orientation ($\eta$) correlation function measurements and uncertainties from halo occupation distribution-based mock galaxy catalogs. We find strong Pearson correlation values with the model across all three correlation functions and further predict aleatoric uncertainties through a mean-variance estimation training procedure. $\xi(r)$ predictions are generally accurate to $\leq10\%$. Our model also successfully captures the underlying signal of the noisier correlations $\omega(r)$ and $\eta(r)$, although with a lower average accuracy. We find that the model performance is inhibited by the stochasticity of the data, and will benefit from correlations averaged over multiple data realizations. Our code will be made open source upon journal publication.