Neural Post-Einsteinian Framework for Efficient Theory-Agnostic Tests of General Relativity with Gravitational Waves

The parametrized post-Einsteinian (ppE) framework and its variants are widely used by the gravitational-wave community to probe gravity through tests that apply to a large class of theories beyond general relativity. However, the ppE formalism is not truly theory-agnostic as it only captures certain types of deviations from general relativity: those that admit a post-Newtonian series representation. 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. In this talk, I will present the neural post-Einsteinian (npE) framework, an extension of the ppE formalism that overcomes the above weaknesses using deep-learning neural networks. I will 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, I will demonstrate the use of the npE framework to efficiently explore deviations from general relativity beyond what can be mapped by the ppE formalism, including modifications coming from higher-order curvature corrections to the Einstein-Hilbert action and dark-photon interactions in possibly hidden sectors of matter.