A physics experiment design competition for gravitational-wave detectors
Team
Laurin Sefa1,3Soham Basu1Priya Kanagasabapathi1Sören Arlt1,2Xuemei Gu4Thomas Christie1Colin Doumont1Andreas Freise5Rana Adhikari6Philipp Hennig1Mario Krenn1
- Department for Computer Science, Faculty of Science, University of Tübingen, Tübingen, Germany
- Feyer, Tübingen, Germany
- Zuse School ELIZA, Darmstadt, Germany
- Institut für Festkörpertheorie und Optik, Friedrich-Schiller-Universität Jena, Jena, Germany
- Nikhef, National Institute for Subatomic Physics, Amsterdam, The Netherlands
- Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA
Submit optimization algorithms, not fixed designs. Each method tunes roughly 200 continuous parameters for a detector topology under a 4-hour evaluation budget using a differentiable simulator.
Central question
Can machine learning discover experimental designs that go beyond
human intuition while remaining physically meaningful
and experimentally constrained?
Simulator
Differometor
JAX-based autodifferentiable simulator for gravitational-wave detector design.
Design archive
30,000 high-quality designs
Released detector blueprints for learning, warm starts, and search.
Prize pool
EUR 25,000
Sponsored by SPRIN-D for the top-performing methods on hidden topologies.