Study of star clusters in the M83 galaxy with a convolutional neural network

TL;DR

This study employs a convolutional neural network to efficiently identify and analyze star clusters in the galaxy M83, revealing age gradients, spatial distributions, and structural properties consistent with galaxy evolution theories.

Contribution

The paper introduces a CNN-based pipeline for rapid detection and parameter inference of star clusters in galaxy images, applied to M83 with extensive results.

Findings

Detected 3,380 star cluster candidates in M83

Confirmed age gradient across spiral arms consistent with density wave theory

Identified spatial distribution patterns related to cluster age and dust lanes

Abstract

We present a study of evolutionary and structural parameters of star cluster candidates in the spiral galaxy M83. For this we use a convolutional neural network trained on mock clusters and capable of fast identification and localization of star clusters, as well as inference of their parameters from multi-band images. We use this pipeline to detect 3,380 cluster candidates in Hubble Space Telescope observations. The sample of cluster candidates shows an age gradient across the galaxy's spiral arms, which is in good agreement with predictions of the density wave theory and other studies. As measured from the dust lanes of the spiral arms, the younger population of cluster candidates peaks at the distance of $\sim$0.4 kpc while the older candidates are more dispersed, but shifted towards $\gtrsim$0.7 kpc in the leading part of the spiral arms. We find high extinction cluster candidates positioned in the trailing part of the spiral arms, close to the dust lanes. We also find a large number of dense older clusters near the center of the galaxy and a slight increase of the typical cluster size further from the center.