## 3 Results  
Results from this study indicate the frequency-based background suppression method described above is well suited for removing unwanted clutter to detect targets in simulated event data. Figure 12 shows the simulated targets used in this work without an overwhelming source present as well as their frequencies. These targets were used for all four scenarios. Figure 13 shows the data before and after this method was applied to a reduced region of interest (ROI) in the weakest (top) and strongest (bottom) overwhelming source data sets. The figures on the left are sums of the events in each pixel before background suppression. This visualization technique is commonly used for event data, though usually applied in smaller time steps to create video frames. The figures on the right are sums of the frequency amplitudes of the detected targets (normalized) after the background suppression and target detection. A reduced ROI (73x46 pixels) was used because including more pixels in the analysis, especially irrelevant pixels, decreases separability between the overwhelming source and targets in the eigenspace. This does not mean prior knowledge of the targets or their locations are required, only that limiting the number of irrelevant pixels improves the results. Figure 12: Sum of events of the simulated targets used in this work without an overwhelming source present as well as their frequencies. As the figures show, this method identifies the targets well except when they are located near the center of the overwhelming source (where it stays bright and does not flicker greatly) and/or when they have a lower frequency. The lack of detection near the center of the overwhelming source was expected as the source was so intense and unchanging that relatively few events were generated in those pixels. The lower frequency targets were also harder to detect as they have a smaller event rate than higher-frequency targets and are more likely to be undersampled in the presence of the overwhelming source. Figure 14 depicts this observed detection trend in the scenario with the strongest overwhelming source. For each of the four scenarios, Table 1 shows the number of PCs used for background suppression (as determined by the automatic angle-based elbow method), false alarm ratio, and detection ratios for the PCBS, MD, and DBSCAN detectors. The MD and DBSCAN detection thresholds were chosen to match the PCBS method's false-alarm ratios. The detection ratios were determined based on the assumption of a single pixel target. The false alarm ratios excluded pixels containing the targets as well as a one pixel guard band to account for blur due to the modeled detector and optical system. Figure 13: Before and after background suppression and target detection for the scenarios containing Overwhelming Source 1 (**top**) and Overwhelming Source 4 (**bottom**). The figures on the left are sums of the events in each pixel before background suppression and the figures on the right are sums of the frequency amplitudes of the detected targets (normalized) after the background suppression and target detection. The DBSCAN method on a standard personal computer could not handle the scenarios with the two brightest overwhelming sources due to out of memory errors caused by the large number of events (greater than 38 and 91 million, respectively). Additionally, the DBSCAN method did not detect any targets at the false-alarm ratio of the PCBS method because the event cluster-based algorithm could not discriminate between the overlapping overwhelming source and targets. Since the detection ratios were zero for the PCBS false-alarm ratios, Table 2 shows the false-alarm and detection ratios when the DBSCAN results are mapped to a binary detection decision. This binary decision resulted in detection of all targets, but with an unacceptable number of false-alarms.