Skymap (IMAGE) Max Planck Institute for Intelligent Systems Caption The new machine-learning algorithm accurately estimates all parameters characterizing a binary black hole source in only a few seconds. The figure on the left shows the sky positions inferred for eight events from the first and second LIGO/Virgo observing run. This compares the estimate using machine learning (colored) to the much slower standard method (gray). On the right, we show four inferred parameters (chirp mass - the effective mass of the binary system, mass ratio, and two spin parameters) for GW150914 (machine learning in orange, standard approach in blue). Credit © M. Dax (Max Planck Institute for Intelligent Systems), S. R. Green, J. Gair (Max Planck Institute for Gravitational Physics), J. H. Macke (Max Planck Institute for Intelligent Systems), A. Buonanno Max Planck Institute for Gravitational Physics), B. Schölkopf (Max Planck Institute for Intelligent Systems) Usage Restrictions image is free to use in any publication License Original content Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.