Neutron stars (NSs) probe the high-density regime of the nuclear equation of
state (EOS). However, inferring the EOS from observations of NSs is a
computationally challenging task. In this work, we efficiently solve this
inverse problem by leveraging differential programming in two ways. First, we
enable full Bayesian inference in under one hour of wall time on a GPU by using
gradient-based samplers, without requiring pre-trained machine learning
emulators. Moreover, we demonstrate efficient scaling to high-dimensional
parameter spaces. Second, we introduce a novel gradient-based optimization
scheme that recovers the EOS of a given NS mass-radius curve. We demonstrate
how our framework can reveal consistencies or tensions between nuclear physics
and astrophysics. First, we show how the breakdown density of a metamodel
description of the EOS can be determined from NS observations. Second, we
demonstrate how degeneracies in EOS modeling using nuclear empirical parameters
can influence the inverse problem during gradient-based optimization. Looking
ahead, our approach opens up new theoretical studies of the relation between NS
properties and the EOS, while effectively tackling the data analysis challenges
brought by future detectors.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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