★ Computational Discovery of Interferometric Gravitational Wave Detectors ★

          @misc{ scivideos_oai:cds.cern.ch:3025627,
            doi = {},
            url = {https://videos.cern.ch/record/3025627},
            author = {},
            keywords = {},
            language = {en},
            title = {* Computational Discovery of Interferometric Gravitational Wave Detectors *},
            publisher = {},
            year = {2026},
            month = {may},
            note = {oai:cds.cern.ch:3025627 see, \url{https://scivideos.org/cern-cds/3025627}}
          }
          

Abstract

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.

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