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Machine learning algorithms can find anomalous needles in cosmic haystacks

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The face of astronomy is changing. Though narrow-field point-and-shoot astronomy still matters (JWST anyone?), large wide-field surveys promise to be the powerhouses of discovery in the coming decades, especially with the advent of machine learning.

A recently developed machine learning program, called ASTRONOMALY, scanned nearly four million galaxy images from the Dark Energy Camera Legacy Survey , discovering 1,635 anomalies including 18 previously unidentified sources with"highly unusual morphology." It is a sign of things to come: a partnership between humans and software that can do better observational science than either could do on their own.

detection programs were trained on test datasets, teaching the algorithm to look for specific phenomena. The limitation of these programs is that they tend to find many anomalies of the same type, rather than entirely new anomalies.

 

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