Digital Discovery of interferometric Gravitational Wave Detectors

TL;DR

This paper uses AI to explore and identify innovative gravitational wave detector designs, revealing superior configurations and a diverse set of solutions that could advance astrophysical observations.

Contribution

It demonstrates AI-driven systematic exploration of detector configurations, uncovering novel topologies that outperform existing designs under realistic constraints.

Findings

Discovered over 50 superior detector solutions.

Revealed novel detector topologies outperforming current designs.

Created a publicly available Gravitational Wave Detector Zoo.

Abstract

Gravitational waves, detected a century after they were first theorized, are spacetime distortions caused by some of the most cataclysmic events in the universe, including black hole mergers and supernovae. The successful detection of these waves has been made possible by ingenious detectors designed by human experts. Beyond these successful designs, the vast space of experimental configurations remains largely unexplored, offering an exciting territory potentially rich in innovative and unconventional detection strategies. Here, we demonstrate the application of artificial intelligence (AI) to systematically explore this enormous space, revealing novel topologies for gravitational wave (GW) detectors that outperform current next-generation designs under realistic experimental constraints. Our results span a broad range of astrophysical targets, such as black hole and neutron star mergers, supernovae, and primordial GW sources. Moreover, we are able to conceptualize the initially unorthodox discovered designs, emphasizing the potential of using AI algorithms not only in discovering but also in understanding these novel topologies. We've assembled more than 50 superior solutions in a publicly available Gravitational Wave Detector Zoo which could lead to many new surprising techniques. At a bigger picture, our approach is not limited to gravitational wave detectors and can be extended to AI-driven design of experiments across diverse domains of fundamental physics.