Rank-Based Detection of Gravitational-Wave Transients Using Chatterjee Correlation.

We present a rank-based method for detecting short-duration gravitational-wave transients in 46 days of coincident data from the first Advanced LIGO observing run (O1). The method applies a moving-window implementation of Chatterjee's rank correlation coefficient to whitened interferometric sensor strain data. This produces a computationally efficient statistic sensitive to temporally ordered signal structure without relying on waveform templates. Compared with traditional excess power and coherent burst searches, the rank-based formulation is potentially less sensitive to certain non-Gaussian noise transients. Furthermore, it processes dual-detector data faster than real time on a single CPU core. We evaluate the method using 60 hardware injections from the O1 dataset, recovering 28 compact binary coalescence injections, primarily for events with a single-detector signal-to-noise ratio above approximately 13. The pipeline identifies 31 transient candidates, including the astrophysical event GW150914 and two instrumental glitches. Although the present implementation is less sensitive than established search pipelines, these results demonstrate the feasibility of the new method. Rank-based detection statistics provide a computationally efficient and complementary method for low-latency transient detection in interferometric sensor networks.