arXiv:2403.18936v1 Announce Type: new
Abstract: 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.
Exploring Gravity Beyond General Relativity: The Neural Post-Einsteinian (npE) Framework
Gravity, one of the fundamental forces of nature, has been extensively studied and validated through the lens of general relativity. However, the limitations of this theory and the need to explore alternative explanations have led to the development of frameworks like the parametrized post-Einsteinian (ppE) framework. While the ppE framework has been valuable in probing deviations from general relativity, it has certain limitations that restrict its scope and efficiency.
The npE framework, introduced in this article, extends the ppE formalism by harnessing the power of deep-learning neural networks. By utilizing a variational autoencoder, the npE framework maps the discrete ppE theories into a continuous latent space. This innovative design allows for the simultaneous testing of multiple theories and the identification of the theory that best fits the observation through a single parameter estimation run.
Unlike the ppE framework, the npE framework is capable of exploring a broader range of deviations from general relativity. It can efficiently search for modifications beyond those captured by the post-Newtonian series representation, such as higher-order curvature corrections to the Einstein-Hilbert action and dark-photon interactions in hidden sectors of matter.
To demonstrate the potential of the npE framework, we showcase its application to future tests of general relativity with the fifth observing run of the LIGO-Virgo-KAGRA collaboration. This collaboration aims to detect gravitational waves and study their properties with unprecedented precision. The npE framework, with its enhanced capability to explore a wider range of theories, offers an invaluable tool in unraveling the mysteries of gravity.
Roadmap for Readers:
- Understanding the Limitations of the ppE Framework: Explore the constraints and computational inefficiencies associated with the ppE framework in capturing deviations from general relativity.
- Introducing the npE Framework: Learn about the neural post-Einsteinian framework and its core component, the variational autoencoder, which enables efficient testing of multiple theories.
- Advantages of the npE Framework: Understand how the npE framework overcomes the limitations of the ppE framework by exploring a broader range of deviations.
- Showcasing the npE Framework: Discover the application of the npE framework in future tests of general relativity with the LIGO-Virgo-KAGRA collaboration’s fifth observing run.
- Potential Challenges and Opportunities: Explore the challenges that may arise in implementing the npE framework and the opportunities it presents in uncovering new insights about gravity.
Challenges: The implementation of the npE framework may require substantial computational resources and expertise in deep learning. Additionally, the theoretical interpretation of potential deviations detected by the npE framework may pose challenges in understanding the underlying physics.
Opportunities: The npE framework offers a more comprehensive and efficient approach to exploring gravity beyond general relativity. It has the potential to uncover new phenomena and shed light on the mysteries of dark matter, dark energy, and other fundamental aspects of the universe.