Speaker
Description
We examine the robustness of two common sensitivity metrics for GW detection pipelines, using AresGW model 1, an ML-based detection code. By analyzing the number of detected waveform injections in real detector noise for multiple month-long datasets and the sensitive distance at different false-alarm-ratio (FAR) thresholds, we investigate how these sensitivity metrics fluctuate due to dataset variability. In addition, we evaluate the dependence of these two metrics on contamination by astrophysical GW signals. We also develop a fully calibrated analytical model that explains the observed variance, using Poisson statistics, detector non-stationarity, and the chirp-mass weighting of the sensitive distance. The model identifies threshold variance driven by non-stationarity as the dominant source of variability in the detection count, and provides a first-principles decomposition of the suppression mechanism that makes the sensitive distance a more stable comparison metric. Our findings highlight the challenges introduced by finite-duration datasets and emphasize the need for more rigorous statistical validation. By identifying these challenges, we aim to clarify the practical limitations of both ML-based and traditional detection systems and inform future benchmarking standards for GW searches.