Peculiar Velocity Estimation from Kinetic SZ Effect using Deep Neural Networks

TL;DR

This paper introduces a deep learning method to estimate galaxy cluster velocities from the kinetic SZ effect, bypassing optical depth estimation and improving accuracy over traditional analytical methods.

Contribution

It presents a novel deep neural network approach trained on simulations to accurately estimate peculiar velocities from kinetic SZ data.

Findings

Deep learning improves velocity estimation accuracy by 17% over analytical methods.

Model is robust across different noise conditions.

Uses large cosmological hydrodynamical simulations for training.

Abstract

The Sunyaev-Zel'dolvich (SZ) effect is expected to be instrumental in measuring velocities of distant clusters in near future telescope surveys. We simplify the calculation of peculiar velocities of galaxy clusters using deep learning frameworks trained on numerical simulations to avoid the estimation of the optical depth. The image of distorted photon backgrounds are generated for idealized observations using one of the largest cosmological hydrodynamical simulations, the Magneticum simulations. The model is tested to be capable peculiar velocities from future kinetic SZ observations under different noise conditions. The deep learning algorithm displays robustness in estimating peculiar velocities from kinetic SZ effect by an improvement in accuracy of about 17% compared to the analytical approach.