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