Speaker
Description
Current and next-generation gravitational wave detectors are designed by human experts who must balance coupled physical effects across many domains. The vast space of all possible experiment designs suggests that many high-sensitivity, unconventional detectors may lie beyond the reach of human intuition alone. AI-based methods are increasingly capable of discovering powerful measurement schemes from first principles, offering a complementary design paradigm with biases distinct from those of human experts. We therefore frame the discovery of novel gravitational wave measurement techniques as a search for optima over a vast space of hardware configurations subject to practical constraints. We discuss how to engineer an expressive search space with the potential to discover novel detector topologies and present Differometor, a differentiable interferometer simulator built for high-performance optimization. We then formulate gravitational wave detector design as a challenging algorithmic benchmark and argue that new interpretability and analysis tools will be essential for understanding and exploiting unconventional AI-discovered detector blueprints.