The complex system response of 3D position sensitive gamma-ray detectors complicates the model for the recorded measurements and makes exact expressions for detection performance intractable. This makes source detection performance difficult and expensive to com pute. Asymptotic analysis has the potential to simplify detection performance prediction with complex systems and has previously been applied to detection performance prediction with simulated gamma-ray detectors. In this work, we use asymptotic performance prediction methods to predict points on the receiver operating characteristic (ROC) curve for the illustrative task of detecting a Cs-137 source in background with an I8-detector CdZnTe array.We assume that the source position, background spectrum, and background spatial distribution are known. Our results show that the asymptotic performance prediction method accurately predicts the empirically observed performance even with real data recorded with a real system. Our results also characterize the performance of the detector array for the task of source detection. The accuracy and computational efficiency of the asymptotic detection performance prediction method make it a viable alternative to empirical performance evaluation.Gamma-ray source detection problems arise in se curity screening, nuclear nonproliferation, and medical diagnostics. Simple systems for radioactive source detec tion look for an increase in the rate of received photons due to a radiation source. More complex measurement systems use spatial and spectral information to achieve better performance, but these systems often have a com plicated system response, making it difficult to compute detection performance analytically.In this work, we quantify detection performance in terms of the receiver operating characteristic (ROC) curve, which is the probability of detection as a function of the probability of false alarm [1].that characterized the detection performance of garnma ray detectors relied on empirical ROC calculation, e.g.,[2], [3]. Empirical ROC calculation is computationally expensive and provides only limited intuition about how detector or environment parameters affect detection performance.Asymptotic ROC prediction is a computationally ef ficient alternative to empirical ROC computation for likelihood-based tests, or tests that are functions of estimates obtained by maximizing a modeled likelihood. We developed asymptotic approximations for the distri butions of likelihood-based estimates in [4], and used these approximations to predict detection performance in the presence of model mismatch. It was shown in [4] that the asymptotic performance prediction method yields more accurate predictions in terms of mean square error than empirical methods, especially when few measurements are available.The simulation results of [4] do not demonstrate that the proposed method can accurately predict the perfor mance of real detectors. In this work, we show that the performance prediction method that accounts for model mismatch develope...