Machine learning models are increasingly augmenting human processes, either performing repetitious tasks faster or providing some systematic insight that helps put human knowledge in perspective. Astronomers at UC Berkeley were surprised to find both happen after modeling gravitational microlensing events, leading to a new unified theory for the phenomenon.

Gravitational lensing occurs when light from far-off stars and other stellar objects bends around a nearer one directly between it and the observer, briefly giving a brighter — but distorted — view of the farther one. Depending on how the light bends (and what we know about the distant object), we can also learn a lot about the star, planet or system that the light is bending around.

For example, a momentary spike in brightness suggests a planetary body transiting the line of sight, and this type of anomaly in the reading, called a "degeneracy" for some reason, has been used to spot thousands of exoplanets.

Due to the limitations of observing them, it's difficult to quantify these events and objects beyond a handful of basic notions like their mass. And degeneracies are generally considered to fall under two possibilities: that the distant light passed closer to either the star or the planet in a given system. Ambiguities are often reconciled with other observed data, such as that we know by other means that the planet is too small to cause the scale of distortion seen.

UC Berkeley doctoral student Keming Zhang was looking into a way to quickly analyze and categorize such lensing events, as they appear in great number as we survey the sky more regularly and in greater detail. He and his colleagues trained a machine learning model on data from known gravity microlensing events with known causes and configurations, then set it free on a bunch of others less well quantified.

The results were unexpected: in addition to deftly calculating when an observed event fell under one of the two main degeneracy types, it found many that didn't.