Impact of Dynamical Tides on the Reconstruction of the Neutron Star Equation of State

Gravitational waves (GWs) from inspiraling neutron stars afford us a unique opportunity to infer the as-of-yet unknown equation of state of cold hadronic matter at supranuclear densities. During the inspiral, the dominant matter effects arise due to the star's response to their companion's tidal field, leaving a characteristic imprint in the emitted GW signal. This unique signature allows us to constrain the cold neutron star equation of state. At GW frequencies above ≳800  Hz, however, subdominant tidal effects known as dynamical tides become important. In this Letter, we demonstrate that neglecting dynamical tidal effects associated with the fundamental (f) mode leads to large systematic biases in the measured tidal deformability of the stars and hence in the inferred neutron star equation of state. Importantly, we find that f-mode dynamical tides will already be relevant for Advanced LIGO's and Virgo's fifth observing run (∼2025)-neglecting dynamical tides can lead to errors on the neutron radius of O(1  km), with dramatic implications for the measurement of the equation of state. Our results demonstrate that the accurate modeling of subdominant tidal effects beyond the adiabatic limit will be crucial to perform accurate measurements of the neutron star equation of state in upcoming GW observations.

Prospects for Measuring the Hubble Constant with Neutron-Star-Black-Hole Mergers

Gravitational wave (GW) and electromagnetic (EM) observations of neutron-star-black-hole (NSBH) mergers can provide precise local measurements of the Hubble constant (H_{0}), ideal for resolving the current H_{0} tension. We perform end-to-end analyses of realistic populations of simulated NSBHs, incorporating both GW and EM selection for the first time. We show that NSBHs could achieve unbiased 1.5%-2.4% precision H_{0} estimates by 2030. The achievable precision is strongly affected by the details of spin precession and tidal disruption, highlighting the need for improved modeling of NSBH mergers.