A group of researchers from the Max Planck Institute for Intelligent Systems (MPI-IS) in Tübingen and the Institute for Gravitational Physics (AEI) in Potsdam presented a neural network and used it to infer all the properties of a binary black hole.
The new algorithm is called DINGO (deep inference for gravitational wave observations). In a few seconds, the network displays the size, spins and all parameters that describe black holes. Scientists have performed this analysis for the first time with such speed and accuracy.
“We can determine in a matter of seconds how large and massive the two black holes that generated gravitational waves when they merged were. Judging by the same refer to the speed of rotation of the black hole, distance from the Earth and the source of the gravitational wave, ”explains Maximilian Dax, the first author of the study.
Black holes are one of the mysteries of the universe. They emit gravitational radiation – the vibrations of space and time that Albert Einstein predicted in 1916. This causes the orbit to become faster and denser, causing the black holes to coalesce in the last burst of radiation. These gravitational waves travel through the universe at the speed of light, and some observatories record them. Scientists are comparing this data with theoretical predictions to estimate the properties of the source, including the size of black holes and their speed of rotation.
The new method will be useful for studying complex gravitational wave signals, binary black hole configurations and binary neutron stars. While the collision of black holes releases energy in the form of gravitational waves, merging neutron stars also emit radiation in the electromagnetic spectrum. To see them through telescopes, you need to very quickly determine where the gravitational wave is coming from. The DINGO method can be used to predict collisions of neutron stars with a black hole.