Hierarchical Bayesian inference allows accurate recovery of intrinsic astrophysical source populations even when follow-up selection is unknown and correlated with parameters of interest.
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Simulations indicate that catalogs of more than 200 events are needed to precisely measure the neutron-star mass fraction f_NS(m) and over 100 events to rule out all low-mass objects being black holes using gravitational-wave data alone.
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What You Don't Know Won't Hurt You: Self-Consistent Hierarchical Inference with Unknown Follow-up Selection Strategies
Hierarchical Bayesian inference allows accurate recovery of intrinsic astrophysical source populations even when follow-up selection is unknown and correlated with parameters of interest.
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Distinguishing between Black Holes and Neutron Stars within a Population of Weak Tidal Measurements
Simulations indicate that catalogs of more than 200 events are needed to precisely measure the neutron-star mass fraction f_NS(m) and over 100 events to rule out all low-mass objects being black holes using gravitational-wave data alone.