What's Inside a Neutron Star: AI Reveals It by Weight and Size ✛ ✦

Original: "Constraining the High-Density Equation of State with Present and Future NICER Observations Using Physics-Informed Regularized Machine Learning"
arXiv:2607.12722 · 2026-07-14 · CC BY 4.0 · 1 min
Artificial intelligence reveals the interior of neutron stars in a second from their mass and radius.

Neutron stars are the remnants of dead giants, crushed into a sphere the size of a city. Their mass and radius are known from observations of lighthouse-like pulsars and gravitational waves (ripples in space), picked up by the LIGO observatory using giant laser 'rulers'. But the composition of their interiors remains a mystery, like trying to guess the fruit inside a sealed box just by its weight and size. The old method was like brute-forcing every conceivable filling with a computer (computer simulations) until one matched.

Inside this 'fruit', sound races almost as fast as light: under monstrous pressure, the speed of sound reaches 60% of the speed of light — only light itself is faster.

Now a neural network trained on thousands of examples instantly spits out pressure and density from mass and radius, like a seasoned gourmand guessing the contents by the box's weight. It strictly obeys the laws of physics, passed down by Einstein, Schwarzschild, and Chandrasekhar. This instant analysis points out which stars to watch first to unravel the secret of the densest matter.

Under monstrous pressure inside a neutron star, sound travels at 60% the speed of light—180,000 km/s, almost nothing is faster.

The sci-fi novel 'Dragon's Egg' describes intelligent life on the surface of a neutron star. Today, neural networks help figure out if even hints of such a thing could exist there.

\frac{dP}{dr} = -\frac{G m(r) \rho(r)}{r^2} \left[1+\frac{P(r)}{\rho(r)c^2}\right] \left[1+\frac{4\pi r^3 P(r)}{m(r)c^2}\right] \left[1-\frac{2Gm(r)}{rc^2}\right]^{-1}
Relates the pressure gradient P(r) to the mass m(r), density ρ(r), and radius r within the framework of general relativity.
c_s^2 = \frac{dP}{d\epsilon}
Parameter characterizing the stiffness of the equation of state; must satisfy 0 ≤ c_s^2 ≤ 1.
Scientists
Paul DiracEnrico FermiDavid DeutschErwin SchrödingerJohn Stewart BellLouis de Broglie
Concepts
physics informed neural network nicer neural network regression autoencoder neutron star equation of state pattern recognition generalization inelastic neutron scattering regularisation robust machine learning binning astrophysical signal detection effective number of neutrino species self-supervised learning neutron star structure regularization methods medical image analysis