Researchers at the University of Coimbra, in collaboration with scientists from China and India, have achieved a significant breakthrough in understanding neutron stars by applying machine learning techniques. Neutron stars, among the densest objects in the universe, present a puzzle regarding their true composition.
The team employed symbolic regression, a machine learning method, to identify algebraic relationships between a neutron star's maximum mass and its equation of state. This innovative approach significantly reduces the computational time required to identify models that align with astronomical observations, speeding up the process by a factor of seven.
Scientists hope to utilize advanced computational techniques to decode the equation of state of dense matter directly from neutron star observables. This could reveal the properties of baryonic matter at extreme densities and determine when quarks become deconfined from nucleons. Understanding the equation of state of nuclear matter under these extreme conditions is crucial for interpreting observations of neutron stars, supernova explosions, and neutron star mergers.