Identifying Astrophysical Anomalies in 99.6 Million Cutouts from the Hubble Legacy Archive Using AnomalyMatch

TL;DR

This paper introduces AnomalyMatch, a semi-supervised method that rapidly analyzes nearly 100 million Hubble image cutouts, discovering numerous astrophysical anomalies like gravitational lenses and galaxy mergers, demonstrating its efficiency for large-scale surveys.

Contribution

The paper presents a novel semi-supervised and active learning approach, AnomalyMatch, for efficient detection of rare astrophysical phenomena in large astronomical datasets.

Findings

Discovered 138 candidate gravitational lenses

Identified 18 jellyfish galaxies

Detected 417 merging or interacting galaxies

Abstract

Astronomical archives contain vast quantities of unexplored data that potentially harbour rare and scientifically valuable cosmic phenomena. We leverage new semi-supervised methods to extract such objects from the Hubble Legacy Archive. We have systematically searched approximately 100 million image cutouts from the entire Hubble Legacy Archive using the recently developed AnomalyMatch method, which combines semi-supervised and active learning techniques for the efficient detection of astrophysical anomalies. This comprehensive search rapidly uncovered a multitude of astrophysical anomalies presented here that significantly expand the inventory of known rare objects. Among our discoveries are 138 new candidate gravitational lenses, 18 jellyfish galaxies, and 417 mergers or interacting galaxies. The efficiency and accuracy of our iterative detection strategy allows us to trawl the complete archive within just 2-3 days, highlighting its potential for large-scale astronomical surveys. We present a detailed overview of these newly identified objects, discuss their astrophysical significance, and demonstrate the considerable potential of AnomalyMatch to efficiently explore extensive astronomical datasets, including, e.g., upcoming Euclid data releases.