Astronomaly at scale: searching for anomalies amongst 4 million galaxies

Etsebeth, V.; Lochner, M.; Walmsley, M.; Grespan, M.

doi:10.1093/mnras/stae496

Astronomaly at scale: searching for anomalies amongst 4 million galaxies

Journal Article · Thu Feb 22 00:00:00 EST 2024 · Monthly Notices of the Royal Astronomical Society

DOI:https://doi.org/10.1093/mnras/stae496· OSTI ID:2318767

; ; ; Grespan, M.

ABSTRACT

Modern astronomical surveys are producing data sets of unprecedented size and richness, increasing the potential for high-impact scientific discovery. This possibility, coupled with the challenge of exploring a large number of sources, has led to the development of novel machine-learning-based anomaly detection approaches, such as astronomaly. For the first time, we test the scalability of astronomaly by applying it to almost 4 million images of galaxies from the Dark Energy Camera Legacy Survey. We use a trained deep learning algorithm to learn useful representations of the images and pass these to the anomaly detection algorithm isolation forest, coupled with astronomaly’s active learning method, to discover interesting sources. We find that data selection criteria have a significant impact on the trade-off between finding rare sources such as strong lenses and introducing artefacts into the data set. We demonstrate that active learning is required to identify the most interesting sources and reduce artefacts, while anomaly detection methods alone are insufficient. Using astronomaly, we find 1635 anomalies among the top 2000 sources in the data set after applying active learning, including eight strong gravitational lens candidates, 1609 galaxy merger candidates, and 18 previously unidentified sources exhibiting highly unusual morphology. Our results show that by leveraging the human–machine interface, astronomaly is able to rapidly identify sources of scientific interest even in large data sets.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2318767

Alternate ID(s):: OSTI ID: 2582421

Journal Information:: Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 1 Vol. 529; ISSN 0035-8711

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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