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

Abstract

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. The latter instead, working on catalogue data, could identify objects with unusual values of magnitudes and colours, which in turn could indicate the presence of singularities.

arXiv commentPreprint version of the manuscript to appear in the Volume "Intelligent Astrophysics" of the series "Emergence, Complexity and Computation", Book eds. I. Zelinka, D. Baron, M. Brescia, Springer Nature Switzerland, ISSN: 2194-7287

References in corpus (16)

• Scikit-learn: Machine Learning in Python
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• The Eleventh and Twelfth Data Releases of the Sloan Digital Sky Survey: Final Data from SDSS-III
• A concise reference to (projected) Sersic R^{1/n} quantities, including Concentration, Profile Slopes, Petrosian indices, and Kron Magnitudes
• The fourth data release of the Kilo-Degree Survey: ugri imaging and nine-band optical-IR photometry over 1000 square degrees
• The third data release of the Kilo-Degree Survey and associated data products
• Probabilistic Random Forest: A machine learning algorithm for noisy datasets
• The weirdest SDSS galaxies: results from an outlier detection algorithm
• Deep learning for galaxy surface brightness profile fitting
• Evolution of galaxy size--stellar mass relation from the Kilo Degree Survey
• Catalog of quasars from the Kilo-Degree Survey Data Release 3
• Return of the features. Efficient feature selection and interpretation for photometric redshifts
• Photometric redshifts for X-ray-selected active galactic nuclei in the eROSITA era
• Classification and Recovery of Radio Signals from Cosmic Ray Induced Air Showers with Deep Learning
• Data Deluge in Astrophysics: Photometric Redshifts as a Template Use Case
• Redshifted broad absorption line quasars found via machine-learned spectral similarity

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