The assumption that the signal-to-background density ratio consists solely of low-frequency features presents several limitations. One significant limitation is that this assumption may overlook important high-frequency variations that could indicate the presence of signals. In scenarios where the signal exhibits sharp peaks or intricate structures in the feature space, relying exclusively on low-frequency components may lead to missed detections or false negatives.
To adapt the method for more complex signal topologies, one could implement a hybrid approach that combines both low-pass and high-pass filtering techniques. By analyzing both the low-frequency and high-frequency components of the density ratio, the method can be fine-tuned to identify signals that may be masked by background noise or that exhibit complex distributions.
Additionally, employing a multi-resolution analysis, such as wavelet transforms, could allow for the simultaneous examination of both low and high-frequency features. This would enable the detection of signals with varying scales and complexities, enhancing the method's sensitivity to diverse signal topologies. Furthermore, incorporating domain knowledge about the expected signal characteristics could guide the selection of appropriate filtering techniques and parameters, improving the overall robustness of the detection process.