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Scientists improve gravitational wave identification with machine learning

A study published in Physical Review Letters outlines a new approach for extracting information from binary systems by looking at the entire posterior distribution instead of making decisions based on individual parameters.

Since their detection in 2015, have become a vital tool for astronomers studying the early universe, the limits of general relativity and cosmic events such as compact .

Binary systems consist of two massive objects, like neutron stars or black holes, spiraling toward each other. As they merge together, they generate ripples in spacetime—gravitational waves—which give us information about both objects.

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