Astrophysical Inferences from Multimessenger Ensembles

Abstract

Nearly a decade from the first detection of gravitational waves, the field of gravitational-wave astronomy is on the cusp of its population-driven era, wherein observations of a diverse ensemble of gravitational-wave and multimessenger sources promise to yield deep insights into the underlying astrophysical processes behind these dynamic phenomena. However, the very aspect of this population-driven era that gives rise to its incredible potential also carries with it a great challenge: the sheer scale and complexity of upcoming gravitational-wave and multimessenger datasets. Reckoning with this challenge will require a concerted, interdisciplinary effort to develop, implement, and execute new analyses that can realize the potential of these immense datasets. This thesis is an exploration of several such efforts, each establishing novel approaches and insights that have the potential to shape the future of the field. It is composed of three distinct parts. The first considers a novel analysis that seeks to constrain the dense nuclear equation of state through hierarchical Bayesian inference of an ensemble of subthreshold binary neutron star post-merger gravitational wave signals. The second presents detailed estimates of the prospects for multimessenger observations with upcoming space telescopes, and in doing so informs the strategy for electromagnetic follow-up to gravitational-wave events with the UltraViolet EXplorer, a major NASA mission of the 2030's. The final portion of the thesis develops a series of novel analyses for Bayesian inference of astrophysical stochastic gravitational wave backgrounds in the Laser Interferometer Space Antenna (LISA), a spaceborne gravitational-wave observatory launching in 2035. These analyses leverage several such signals' anisotropies to separate the distinct contributions of their component astrophysical source populations. In doing so, this work demonstrates for the first time 1) the existence of a previously unknown stochastic signal in LISA from white dwarf binaries in the Large Magellanic Cloud; 2) a prototype simultaneous inference infrastructure for LISA capable of characterizing isotropic and anisotropic stochastic background signals in the presence of the stochastic foreground contribution from white dwarf binaries in the Milky Way; and 3) the potential of LISA to simultaneously infer the distinct stochastic contributions of the white dwarf binary populations of the Milky Way and Large Magellanic Cloud.