高能暂现源 · 每日文献综述

Bayesian inference, widely used in gravitational wave parameter estimation, depends on the choice of priors, i.e., on our previously existing knowledge. However, to investigate neutron star mergers, priors are often chosen in an agnostic way, leaving valuable information from nuclear physics and independent observations of neutron stars unused. In this work, we propose to encode information on neutron star physics into physics-informed prior distributions constructed with normalizing flows. These priors take input from constraints on the nuclear equation of state and neutron star mass distributions. Applied to GW170817, GW190425, and GW230529, we highlight two contributions of the framework. First, we demonstrate its ability to provide source classification and to enable model selection of equation-of-state constraints for loud signals such as GW170817, directly from the gravitational wave data. Second, we obtain narrower constraints on the source properties through these informed priors. As a result, these physics-informed priors consistently recover higher luminosity distances compared to agnostic priors. Our method provides a scalable way for classifying future ambiguous low-mass mergers observed through gravitational waves and for informing single-event gravitational wave data analysis with neutron star physics.