Skip to main content
SearchLoginLogin or Signup

Spectral analysis of the quiescent low-mass X-ray binary in the globular cluster M30

Published onJun 01, 2020
Spectral analysis of the quiescent low-mass X-ray binary in the globular cluster M30

Neutron Stars (NSs) are the most extreme directly observable objects in the Universe. Particularly, in their cores, densities overcome the nuclear matter density. Since those conditions cannot be reproduced on Earth, NSs provide a unique laboratory to study the behaviour of matter at such high densities. Measuring the macroscopic properties of NSs, such as mass and radius, is useful to constrain the equation of state of ultra-dense matter. One promising method to constrain NS radii consists of the study of low-mass X-ray binaries in quiescence (qLMXB), i.e., during periods of low accretion levels. In quiescence, we can model the surface emission as a single-composition atmosphere dominated by light elements. Moreover, when these systems are located in globular clusters we know the distance to them. In this work, we present a recent Chandra observation of the qLMXB in the globular cluster M30, and we analyze it together with a previous observation in 2001. We fit the thermal emission with light-element composition atmosphere models (hydrogen or helium), including absorption by the interstellar medium, correction for pile-up of X-ray photons in the detector, and a power-law to account for count excesses at high energies. We use a Markov Chain Monte Carlo approach to extract the mass-radius credible intervals for both chemical compositions of the atmosphere. The measured radius obtained from a H model is difficult to reconcile with most current physics models and with other NS radii, generally in the 11-14 km range. In contrast, a He atmosphere results in a radius consistent with this range. Finally, we explore possible sources of systematic uncertainty that may result in an underestimation of the radius, identifying the presence of surface temperature inhomogeneities as the most relevant bias.

No comments here