Constraint‐Aware Operating‐Point Selection for FAST Pulsar Candidate Screening

FAST pulsar surveys generate candidate volumes that make manual review a practical bottleneck, so fixed‐threshold classification is often mismatched to deployment under limited review budgets. We formulate candidate screening as a constrained operating‐point selection problem: given model scores, the decision threshold is chosen to maximise recall while satisfying explicit review‐oriented constraints. Each candidate is represented by four diagnostic regions of interest together with a compact one‐dimensional feature vector derived from curve statistics, and several representative scoring models are evaluated within a unified 2D + 1D framework. Thresholds are selected on a calibration split under either a minimum precision constraint or a maximum false‐positive‐rate constraint. Evaluation includes both conventional point metrics and budget‐aware measures, namely, Top‐ K , Top‐ x % and workload–recall curves. Experiments on labelled FAST data show that moderate constrained operating points can provide more practical deployment trade‐offs than a fixed threshold in the evaluated review‐limited setting, particularly by balancing false‐alarm control against pulsar recovery. Budget‐aware evaluation also reveals practical differences among scoring variants that are less visible in fixed‐threshold summaries. These results support treating threshold selection as an explicit deployment component rather than a static postprocessing step in FAST candidate‐screening pipelines.