Abstract:
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With the advancement of probing technology, there has been rapid progress in accurately quantifying the radius and mass distribution of neutron stars. Although the mass distribution of neutron stars has been calibrated with a significantly high precision, precisely estimating the radius continues to pose a stiff challenge. High quality datasets from X-ray satellites have propelled significant progress in theoretical modeling, placing the radius in the 9.9 ? 11.2 km range and shrinking the uncertainty ranges due to a better understanding of the sources of systematic errors. The guiding equation that relates the observables (scattering angle probabilities) to the mass and the radius is called the equation of state, and recovering the mass and radius from noisy observations around the equation of state defines a statistical inverse problem. In this work, we propose a novel probabilistic mechanism to solve this inverse problem that takes into account various sources of uncertainty, integrates them using the laws of Physics, and provides more accurate uncertainty quantification for the neutron star radius.
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