A star trembles like a bell after being struck. These tremors, tiny fluctuations in brightness and surface, can be caught by a telescope. From the frequencies of these 'stellar notes,' astronomers infer what's happening inside: how old the star is, how it's structured. But to decode the melody, one used to have to sift through thousands of models — slow and costly.

Now there's a tool called PITCHFORK — a neural network with a branched architecture. It learns from precomputed stellar models and then, in seconds, outputs both standard parameters (temperature, luminosity, heavy-element fraction) and the frequencies of 35 types of oscillations. Accuracy is high: temperature is predicted with an error of less than six degrees, and frequencies with a margin of just 0.02 percent.

PITCHFORK is combined with a statistical method that accounts for random errors and yields not a single number but a whole range of possible values for each parameter. It's like getting an honest 'between 4.2 and 4.8 billion years' with probabilities attached, instead of a single answer 'the star is 4.5 billion years old.'

The method was tested on the Sun and on the two components of the binary system 16 Cygni. The results matched previous reliable estimates. So the tool can be trusted. In the coming years, new space missions will bring data on millions of stars, and such a fast yet careful neural network will help us not drown in that stream and finally understand how suns evolve in different corners of the Galaxy.