Machine Learning decodes the tremors of the Universe

Neural network analyzes gravitational waves in real time

Researchers trained a neural network to estimate – in just a few seconds – the precise characteristics of merging black holes based on their gravitational-wave emissions. The network determines the masses and spins of the black holes, where in the sky, at what angle, and how far away from Earth the merger took place.

Black holes are one of the greatest mysteries of our Universe – for example a black hole with the mass of our Sun has a radius of only 3 kilometers.  Black holes in orbit around each other give off gravitational radiation — oscillations of space and time predicted by Albert Einstein in 1916. This causes the orbit to become faster and tighter, and eventually the black holes merge in a final burst of radiation. These gravitational waves propagate through the Universe at the speed of light, and are detected by observatories in the USA (LIGO) and Italy (Virgo). Scientists compare the data collected by the observatories against theoretical predictions to estimate the properties of the source, including how large the black holes are and how fast they are spinning. Currently, this procedure takes at least hours, often months…