ORAC: A Detector-Invariant Coherence Statistic for Gravitational-Wave Candidate Ranking, with the Detector Identification Test

Template-free, detector-invariant method for ranking gravitational-wave candidates based on cross-detector coherence excess calibrated against a time-slide background. Includes the Detector Identification Test (DIT), a diagnostic proposed as a standard check for detector-specific confounds in machine-learning GW classifiers. Full validation framework: injection campaign, unmodeled-morphology tests, SNR correlation, blind search, and veto studies. Honestly documents that the method is ~2x less sensitive than established unmodeled pipelines and is suitable as a post-trigger ranker rather than a standalone trigger generator.