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Published December 10, 2019 | Accepted Version + Published
Journal Article Open

A Nicer View of PSR J0030+0451: Implications for the Dense Matter Equation of State


Both the mass and radius of the millisecond pulsar PSR J0030+0451 have been inferred via pulse-profile modeling of X-ray data obtained by NASA's Neutron Star Interior Composition Explorer (NICER) mission. In this Letter we study the implications of the mass–radius inference reported for this source by Riley et al. for the dense matter equation of state (EoS), in the context of prior information from nuclear physics at low densities. Using a Bayesian framework we infer central densities and EoS properties for two choices of high-density extensions: a piecewise-polytropic model and a model based on assumptions of the speed of sound in dense matter. Around nuclear saturation density these extensions are matched to an EoS uncertainty band obtained from calculations based on chiral effective field theory interactions, which provide a realistic description of atomic nuclei as well as empirical nuclear matter properties within uncertainties. We further constrain EoS expectations with input from the current highest measured pulsar mass; together, these constraints offer a narrow Bayesian prior informed by theory as well as laboratory and astrophysical measurements. The NICER mass–radius likelihood function derived by Riley et al. using pulse-profile modeling is consistent with the highest-density region of this prior. The present relatively large uncertainties on mass and radius for PSR J0030+0451 offer, however, only a weak posterior information gain over the prior. We explore the sensitivity to the inferred geometry of the heated regions that give rise to the pulsed emission, and find a small increase in posterior gain for an alternative (but less preferred) model. Lastly, we investigate the hypothetical scenario of increasing the NICER exposure time for PSR J0030+0451.

Additional Information

© 2019. The American Astronomical Society. Received 2019 August 7; revised 2019 September 6; accepted 2019 September 16; published 2019 December 12. This work was supported in part by NASA through the NICER mission and the Astrophysics Explorers Program. T.E.R. and A.L.W. acknowledge support from ERC Starting Grant No. 639217 CSINEUTRONSTAR (PI: Watts). A.L.W. would like to thank Andrew Steiner for useful discussions on the role of priors in previously published results. The authors would also like to thank Kent Wood for helpful comments. This work was sponsored by NWO Exact and Natural Sciences for the use of supercomputer facilities, and was carried out on the Dutch national e-infrastructure with the support of SURF Cooperative. S.K.G., K.H., and A.S. acknowledge support from the DFG through SFB 1245. G.R., T.H., and S.N. are grateful for support from the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) through the VIDI and Projectruimte grants (PI: Nissanke). S.G. acknowledges the support of the CNES. S.M.M. thanks NSERC for support. J.M.L. acknowledges support from NASA through Grant 80NSSC17K0554 and the U.S. DOE from Grant DE-FG02-87ER40317. R.M.L. acknowledges the support of NASA through Hubble Fellowship Program grant HST-HF2-51440.001. This research has made extensive use of NASA's Astrophysics Data System Bibliographic Services (ADS) and the arXiv. Software: Python/C language (Oliphant 2007), GNU Scientific Library (GSL; Gough 2009), NumPy (van der Walt et al. 2011), Cython (Behnel et al. 2011), SciPy (Jones et al. 2001), MPI (Forum 1994), MPI for Python (Dalcín et al. 2008), Matplotlib (Hunter 2007; Droettboom et al. 2018), IPython (Perez & Granger 2007), Jupyter (Kluyver et al. 2016), MultiNest (Feroz et al. 2009), PyMultiNest (Buchner et al. 2014), RNS (Stergioulas & Friedman 1995).

Attached Files

Published - Raaijmakers_2019_ApJL_887_L22.pdf

Accepted Version - 1912.05703.pdf


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