| Abstract
| - Context. Convolutional neural networks (CNNs) have been established as the go-to method for fast object detection and classification of natural images. This opens the door for astrophysical parameter inference on the exponentially increasing amount of sky survey data. Until now, star cluster analysis was based on integral or resolved stellar photometry, which limit the amount of information that can be extracted from individual pixels of cluster images. Aims. We aim to create a CNN capable of inferring star cluster evolutionary, structural, and environmental parameters from multiband images and to demonstrate its capabilities in discriminating genuine clusters from galactic stellar backgrounds. Methods. A CNN based on the deep residual network (ResNet) architecture was created and trained to infer cluster ages, masses, sizes, and extinctions with respect to the degeneracies between them. Mock clusters placed on M 83 Hubble Space Telescope images utilizing three photometric passbands ( F336 W, F438 W, and F814 W) were used. The CNN is also capable of predicting the likelihood of the presence of a cluster in an image and quantifying its visibility (S/N). Results. The CNN was tested on mock images of artificial clusters and has demonstrated reliable inference results for clusters of ages ≲100 Myr, extinctions AV between 0 and 3 mag, masses between 3 × 10 3 and 3 × 10 5M⊙, and sizes between 0.04 and 0.4 arcsec at the distance of the M 83 galaxy. Real M 83 galaxy cluster parameter inference tests were performed with objects taken from previous studies and have demonstrated consistent results.
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