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Published April 20, 2020 | Published + Accepted Version
Journal Article Open

Distinguishing Binary Neutron Star from Neutron Star–Black Hole Mergers with Gravitational Waves

Abstract

The gravitational-wave signal from the merger of two neutron stars cannot be easily differentiated from the signal produced by a comparable-mass mixed binary of a neutron star and a black hole. Indeed, both binary types can account for the gravitational-wave signal GW170817 even if its electromagnetic counterpart emission is taken into account. We propose a method that requires neither information from the post-inspiral phase of the binary nor an electromagnetic counterpart to identify mixed binaries of neutron stars merging with low-mass black holes using gravitational waves alone. This method is based on the fact that certain neutron star properties that can be measured with gravitational waves are common or similar for all neutron stars. For example all neutron stars share the same equation of state, and if the latter is hadronic, neutron stars have similar radii. If a mixed binary is misidentified as a neutron star binary, the inferred neutron star properties will be misestimated and appear as outliers in a population of low-mass binaries. We show that as few as ~5 low-mass events will allow for the identification of the type of one event at the 80% confidence level. We model the population of low-mass binaries with a hierarchical mixture model and show that we can constrain the existence of mixed binaries or measure their abundance relative to neutron star binaries to ~0.1 at the 68% credible level with 100 events.

Additional Information

© 2020 The American Astronomical Society. Received 2020 January 22; revised 2020 April 3; accepted 2020 April 6; published 2020 April 22. We acknowledge valuable discussions with Will Farr, Ramesh Narayan, Yu-Dai Tsai, and Salvatore Vitale. We thank Reed Essick and Francesco Pannarale for useful comments. H.-Y.C. was supported by the black hole Initiative at Harvard University, which is funded by grants from the John Templeton Foundation and the Gordon and Betty Moore Foundation to Harvard University. The Flatiron Institute is supported by the Simons Foundation. Plots in this Letter have been made with matplotlib (Hunter 2007), and we have used stan (Carpenter et al. 2017) to sample the mixture model.

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Published - Chen_2020_ApJL_893_L41.pdf

Accepted Version - 1903.11197.pdf

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