Neural Network Fast-Tracks Galaxy Formation express
Studying the birth of galaxies requires simulating billions of years of dark matter growth and star formation — even supercomputers handle this slowly. The solution came from machine learning: a neural network, like a seasoned pastry chef, from the 'recipe' of dark matter mergers instantly evaluates the future galaxy — its mass, brightness, chemical composition. Training on thousands of virtual universes led to an almost perfect match with real calculations.
An unexpected finding: the details of internal processes turned out to be secondary. A galaxy's fate is almost entirely written in its dark matter history. Accuracy reaches 97%, and speed allows testing thousands of expansion of the universe scenarios in hours, bringing us closer to understanding cosmic evolution.
The most distant galaxies formed when the Universe was less than a billion years old. Their fate was sealed only by the mergers of invisible dark matter — visible stars simply followed this script.
Like Asimov's 'psychohistory,' the neural network predicts the fate of galaxies through mathematical patterns — only here, it's dark matter instead of civilizations.