Posted on by Scott Alan Johnston

Machine Learning Algorithms Can Find Anomalous Needles in Cosmic Haystacks

ESA/Webb, NASA & CSA, J. Rigby.

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 (DECaLS), discovering 1635 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.

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Posted on by Andy Tomaswick

Dark Energy Survey Finds Hundreds of New Gravitational Lenses

It’s relatively rare for a magical object from fantasy stories to have a analog in real life.  A truly functional crystal ball (or palantir) would be useful for everything from military operations to checking up on grandma. While nothing exists to be able to observe the mundanities of everyday life, there is something equivalent for extraordinarily far away galaxies: gravitational lenses.  Now a team led by Xiaosheng Huang from Lawrence Berkeley National Laboratory (LBNL) and several universities around the world have published a list of more than 1200 new gravitational lensing candidates.

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