Projects per year
Abstract
The conventional wisdom, dating back to 2012, is that the mass distribution of Galactic double neutron stars (DNSs) is wellfit by a Gaussian distribution with a mean of 1.33 M _{o} and a width of 0.09 M _{o}. With the recent discovery of new Galactic DNSs and GW170817, the first neutron star merger event to be observed with gravitational waves, it is timely to revisit this model. In order to constrain the mass distribution of DNSs, we perform Bayesian inference using a sample of 17 Galactic DNSs, effectively doubling the sample used in previous studies. We expand the space of models so that the recycled neutron star need not be drawn from the same distribution as the nonrecycled companion. Moreover, we consider different functional forms including uniform, singleGaussian, and twoGaussian distributions. While there is insufficient data to draw firm conclusions, we find positive support (a Bayes factor (BF) of 9) for the hypothesis that recycled and nonrecycled neutron stars have distinct mass distributions. The most probable model  preferred with a BF of 29 over the conventional model  is one in which the recycled neutron star mass is distributed according to a twoGaussian distribution, and the nonrecycled neutron star mass is distributed uniformly. We show that precise component mass measurements of ≈20 DNSs are required in order to determine with high confidence (a BF of 150) whether recycled and nonrecycled neutron stars come from a common distribution. Approximately 60 DNSs are needed in order to establish the detailed shape of the distributions.
Original language  English 

Article number  18 
Number of pages  10 
Journal  The Astrophysical Journal 
Volume  876 
Issue number  1 
DOIs  
Publication status  Published  1 May 2019 
Keywords
 gravitational waves
 methods: data analysis
 pulsars: general
 stars: neutron
Projects
 1 Active

ARC Centre of Excellence for Gravitational Wave Discovery
Bailes, M., McClelland, D. E., Levin, Y., Blair, D. G., Scott, S. M., Ottaway, D. J., Melatos, A., Veitch, P. J., Wen, L., Shaddock, D. A., Slagmolen, B. J. J., Zhao, C., Evans, R. J., Ju, L., Galloway, D., Thrane, E., Hurley, J. R., Coward, D. M., Cooke, J., Couch, W., Hobbs, G. B., Reitze, D., Rowan, S., Cai, R., Adhikari, R. X., Danzmann, K., Mavalvala, N., Kulkarni, S. R., Kramer, M., Branchesi, M., Gehrels, N., Weinstein, A. J. R., Steeghs, D., Bock, D. & Lasky, P.
Monash University – Internal University Contribution, Monash University – Internal Department Contribution
1/01/17 → 31/03/24
Project: Research