Deriving star cluster parameters with convolutional neural networks. II. Extinction and cluster/background classification

Abstract

Context. Convolutional neural networks (CNNs) have been established as the go-to method for fast object detection and classification on 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 limits 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 multi-band images, as well 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 M83 Hubble Space Telescope (HST) images utilizing three photometric passbands (F336W, F438W, and F814W) were used. The CNN is also capable of predicting the likelihood of a cluster's presence in an image, as well as quantifying its visibility (signal-to-noise). Results. The CNN was tested on mock images of artificial clusters and has demonstrated reliable inference results for clusters of ages $\lesssim$100 Myr, extinctions $A_V$ between 0 and 3 mag, masses between $3\times 10^3$ and $3\times 10^5$ ${\mathrm{M}}_{\odot }$, and sizes between 0.04 and 0.4 arcsec at the distance of the M83 galaxy. Real M83 galaxy cluster parameter inference tests were performed with objects taken from previous studies and have demonstrated consistent results.

17 pages, 21 figures

References in corpus (7)

• Rotation-invariant convolutional neural networks for galaxy morphology prediction
• Using H-alpha Morphology and Surface Brightness Fluctuations to Age-Date Star Clusters in M83
• Sizes and Shapes of Young Star Cluster Light Profiles in M83
• An Expanded HST/WFC3 Survey of M83: Project Overview and Targeted Supernova Remnant Search
• Big Fish in Small Ponds: Massive Stars in the Low Mass Clusters of M83
• Deriving physical parameters of unresolved star clusters. II. The degeneracies of age, mass, extinction, and metallicity
• The First Metallicity Study of M83 using the integrated UV light of Star Clusters

Cited by in corpus (1)

• Predicting Images for the Dynamics Of stellar Clusters (π-DOC): a deep learning framework to predict mass, distance and age of globular clusters