TPR, FDR, and FPR metrics computed from the confidence maps of Fig. 11 for a range of confidence thresholds varying from zero to one. Their respective AUCs are shown in each legend. The F1-score is computed at the submitted threshold on the challenge τ_sub = 0.9 (vertical dashed line) and it is shown in the top of each subplot. When the dataset contains injections, TPR and FDR steply decrease with threshold, while FPR decreases monotonically. Thereby, an ideal algorithm would provide a TPR=1, FPR=0 and FDR=0 for any threshold and therefore, an AUC_TPR = 1, AUC_FPR = 0 and AUC_FDR = 0. However, when the dataset does not have injections, the FPR is the only metric that can be defined as it does not depend on TPs.