In 1931, Paul Dirac demonstrated that the existence of magnetic monopoles would explain the quantization of electric charge. Despite decades of searching, no confirmed detection has occurred. Our AI-driven approach leverages deep neural networks trained on simulated monopole signatures to scan petabytes of detector data in real-time.
Our detector array spans 2,847 superconducting quantum interference devices (SQUIDs), each sensitive to flux changes of 10-15 Weber. Neural network ensemble models analyze correlated signals across the array, discriminating monopole signatures from background noise with unprecedented sensitivity.
Since deployment, the system has flagged 23 candidate events exceeding the 5-sigma threshold. Each undergoes rigorous multi-stage verification including cross-correlation analysis, background model subtraction, and independent AI re-evaluation. Current leading candidate: Event #17, detected at 03:47:12 UTC with a confidence score of 99.97%.