Neural Post-Einsteinian Framework for Efficient Theory-Agnostic Tests of General Relativity with Gravitational Waves
arxiv(2024)
摘要
The parametrized post-Einsteinian (ppE) framework and its variants are widely
used to probe gravity through gravitational-wave tests that apply to a large
class of theories beyond general relativity. However, the ppE framework is not
truly theory-agnostic as it only captures certain types of deviations from
general relativity: those that admit a post-Newtonian series representation in
the inspiral of coalescencing compact objects. Moreover, each type of deviation
in the ppE framework has to be tested separately, making the whole process
computationally inefficient and expensive, possibly obscuring the theoretical
interpretation of potential deviations that could be detected in the future. We
here present the neural post-Einsteinian (npE) framework, an extension of the
ppE formalism that overcomes the above weaknesses using deep-learning neural
networks. The core of the npE framework is a variantional autoencoder that maps
the discrete ppE theories into a continuous latent space in a well-organized
manner. This design enables the npE framework to test many theories
simultaneously and to select the theory that best describes the observation in
a single parameter estimation run. The smooth extension of the ppE
parametrization also allows for more general types of deviations to be searched
for with the npE model. We showcase the application of the new npE framework to
future tests of general relativity with the fifth observing run of the
LIGO-Virgo-KAGRA collaboration. In particular, the npE framework is
demonstrated to efficiently explore modifications to general relativity beyond
what can be mapped by the ppE framework, including modifications coming from
higher-order curvature corrections to the Einstein-Hilbert action at high
post-Newtonian order, and dark-photon interactions in possibly hidden sectors
of matter that do not admit a post-Newtonian representation.
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