Comparison of outlier detection methods on astronomical image data
Abstract
Among the many challenges posed by the huge data volumes produced by the new generation of astronomical instruments there is also the search for rare and peculiar objects. Unsupervised outlier detection algorithms may provide a viable solution. In this work we compare the performances of six methods: the Local Outlier Factor, Isolation Forest, k-means clustering, a measure of novelty, and both a normal and a convolutional autoencoder. These methods were applied to data extracted from SDSS stripe 82. After discussing the sensitivity of each method to its own set of hyperparameters, we combine the results from each method to rank the objects and produce a final list of outliers.
Cite
@article{arxiv.2006.08238,
title = {Comparison of outlier detection methods on astronomical image data},
author = {Lars Doorenbos and Stefano Cavuoti and Massimo Brescia and Antonio D'Isanto and Giuseppe Longo},
journal= {arXiv preprint arXiv:2006.08238},
year = {2021}
}
Comments
Preprint version of the accepted 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