Deep Learning nearby galaxy peculiar velocities

TL;DR

This paper demonstrates that convolutional neural networks trained on galaxy images can accurately estimate galaxy distances and peculiar velocities, surpassing traditional methods and enabling better understanding of galaxy dynamics.

Contribution

It introduces a novel approach using CNNs trained on simulated galaxy images with added noise to improve distance and velocity estimations for nearby galaxies.

Findings

CNNs achieved 7.7% fractional RMS distance errors at 75 Mpc.

Estimated RMS peculiar velocity errors are approximately 440 km/s.

Method outperforms traditional distance estimation techniques.

Abstract

We explore how information in images of nearby galaxies can be used to estimate their distance. We train a convolutional Neural Network (NN) to do this, using galaxy images from the Illustris simulation. We show that if the NN is trained on data with random errors added to the true distance (representing training using spectroscopic redshift instead of actual distance), then the NN can predict distances in a test dataset with greater accuracy than it was given in the training set. This is not unusual, as often NNs are trained on data with added noise, in order to increase robustness. In this case, however, it offers a route to estimating peculiar velocities of nearby galaxies. Given a galaxy with a known spectroscopic redshift one can use the NN-predicted distance to make an estimate of the peculiar velocity. Trying this using relatively low resolution (1.4 arcsec per pixel) simulated galaxy images we find fractional RMS distance errors of 7.7% for galaxies at a mean distance of 75 Mpc from the observer, leading to RMS peculiar velocity errors of 440 km/s. In a companion paper we apply the technique to 145,115 nearby galaxies from the NASA Sloan Atlas.