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Neutron star properties depend on both nuclear physics and astrophysical processes, and thus observations of neutron stars offer constraints on both large-scale astrophysics and the behavior of cold, dense matter. In this study, we use astronomical data to jointly infer the universal equation of state of dense matter along with two distinct astrophysical populations: Galactic neutron stars observed electromagnetically and merging neutron stars in binaries observed with gravitational waves. We place constraints on neutron star properties and quantify the extent to which they are attributable to macrophysics or microphysics. We confirm previous results indicating that the Galactic and merging neutron stars have distinct mass distributions. The inferred maximum mass of both Galactic neutron stars, 𝑀pop,EM=2.0⁢5+0.11−0.06⁢𝑀⊙ (median and 90% symmetric credible interval), and merging neutron star binaries, 𝑀pop,GW =1.8⁢5+0.39−0.16⁢𝑀⊙, are consistent with the maximum mass of nonrotating neutron stars set by nuclear physics, 𝑀TOV =2.2⁢8+0.41−0.21⁢𝑀⊙. The radius of a 1.4⁢𝑀⊙ neutron star is 12.2+0.8−0.9  km, consistent with, though ∼20% tighter than, previous results using an identical equation of state model. Even though observed Galactic and merging neutron stars originate from populations with distinct properties, there is currently no evidence that astrophysical processes cannot produce neutron stars up to the maximum value imposed by nuclear physics. 

In this paper we investigate the impact of transient noise artifacts, or glitches, on gravitational- wave inference from ground-based interferometer data, and test how modeling and subtracting these glitches affects the inferred parameters. Due to their time-frequency morphology, broadband glitches cause moderate to significant biasing of posterior distributions away from true values. In contrast, narrowband glitches induce negligible biasing effects, due to distinct signal and glitch morphologies. We inject simulated binary black hole signals into data containing three occurring glitch types from past LIGO-Virgo observing runs, and reconstruct both signal and glitch waveforms using BayesWave, a wavelet-based Bayesian analysis. We apply the standard LIGO-Virgo-KAGRA deglitching pro- cedure to the detector data, which consists of subtracting from calibrated LIGO data the glitch waveform estimated by the joint BayesWave inference. We produce posterior distributions on the parameters of the injected signal before and after subtracting the glitch, and we show that removing the transient noise effectively mitigates bias from broadband glitches. This study provides a baseline validation of existing techniques, while demonstrating waveform reconstruction improvements to the Bayesian algorithm for robust astrophysical characterization in glitch-prone detector data. 

Abstract Neutron stars have solid crusts threaded by strong magnetic fields. Perturbations in the crust can excite nonradial oscillations, which can in turn launch Alfvén waves into the magnetosphere. In the case of a compact binary close to merger involving at least one neutron star, this can happen through tidal interactions causing resonant excitations that shatter the neutron star crust. We present the first numerical study that elucidates the dynamics of Alfvén waves launched in a compact binary magnetosphere. We seed a magnetic field perturbation on the neutron star crust, which we then evolve in fully general-relativistic force-free electrodynamics using a GPU-based implementation. We show that Alfvén waves steepen nonlinearly before reaching the orbital light cylinder, form flares, and dissipate energy in a transient current sheet. Our results predict radio and X-ray precursor emission from this process.