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We have developed a method that maps large astronomical images onto atwo-dimensional map and clusters them. A combination of variousstate-of-the-art machine learning (ML) algorithms is used to develop a fullyunsupervised image quality assessment and clustering system. Our pipelineconsists of a data pre-processing step where individual image objects areidentified in a large astronomical image and converted to smaller pixel images.This data is then fed to a deep convolutional autoencoder jointly trained witha self-organizing map (SOM). This part can be used as a recommendation system.The resulting output is eventually mapped onto a two-dimensional grid using asecond, deep, SOM. We use data taken from ground-based telescopes and, as acase study, compare the system's ability and performance with the resultsobtained by supervised methods presented by Teimoorinia et al. (2020). Theavailability of target labels in this data allowed a comprehensive performancecomparison between our unsupervised and supervised methods. In addition toimage-quality assessments performed in this project, our method can havevarious other applications. For example, it can help experts label images in aconsiderably shorter time with minimum human intervention. It can also be usedas a content-based recommendation system capable of filtering images based onthe desired content.

An astronomical image content-based recommendation system using combined deep learning models in a fully unsupervised mode