Speaker
Description
Neutron stars—pulsars—and their magnetospheres are key sources for multi-messenger astrophysics. Their emission spans the entire electromagnetic spectrum, they are strong candidates for contributing to the cosmic-ray positron excess, and theoretical models (though not yet confirmed observationally) suggest that they may also produce high-energy neutrinos in the TeV–PeV range. By combining these multi-messenger channels, we gain valuable insight into the structure of the pulsar magnetosphere, the physical mechanisms operating under extreme conditions, and the internal properties of the neutron star itself. In this talk, I will present our new methodology for computing the pulsar magnetosphere using machine-learning techniques, specifically Physics-Informed Neural Networks (PINNs). With this method, we achieved the first steady-state 3D solution of the pulsar equation and uncovered a new family of 2D solutions that confirm theoretical constraints which had not been verified until now. I will also show our study of gamma-ray emission from the equatorial current sheet and conclude by demonstrating the flexibility of our framework through comparisons with NICER observations of the stellar polar cap.
