Anomaly detection in Astrophysics: a comparison between unsupervised Deep and Machine Learning on KiDS data

Understand

Every field of Science is undergoing unprecedented changes in the discovery process, and Astronomy has been a main player in this transition since the beginning.

• The ongoing and future large and complex multi-messenger sky surveys impose a wide exploiting of robust and efficient automated methods to classify the observed structures and to detect and characterize peculiar and unexpected sources.
• We performed a preliminary experiment on KiDS DR4 data, by applying to the problem of anomaly detection two different unsupervised machine learning algorithms, considered as potentially promising methods to detect peculiar sources, a Disentangled Convolutional Autoencoder and an Unsupervised Random Forest.
• The former method, working directly on images, is considered potentially able to identify peculiar objects like interacting galaxies and gravitational lenses.