Disentangling Spatial, Temporal, and Space Weather Contributions to Spacecraft Anomaly States: A Case–Control Analysis of GOES-16/17

Recurring spacecraft anomalies may reflect anomaly-prone system states shaped jointly by spacecraft geometry, recent anomaly history, and space weather forcing, rather than isolated responses to single external drivers. Distinguishing these contributions is important for interpretable anomaly monitoring in geostationary orbit. This study develops a satellite-stratified case–control framework for GOES-16/17 Extreme Ultraviolet and X-ray Irradiance Sensors (EXIS) Space Wire (SpW) anomaly records. Orbital–illumination descriptors, same-satellite event history variables, and space weather variables from the NASA OMNI database are integrated within chronological train–test validation, supported by null-baseline comparison, stratified bootstrap confidence intervals, exclusion window sensitivity analysis, feature group ablation, cross-satellite testing, and calibration diagnostics. Orbital–illumination variables provide weak but reproducible discrimination, event history descriptors capture temporal clustering, and space weather variables add complementary held-out information. The full Space Environment-Integrated Model (SEIM) reached a test area under the receiver operating characteristic curve (AUC) of 0.7540, while a compact train-only L1-selected clean Top-15 model achieved comparable held-out discrimination with a test AUC of 0.7588 after excluding direct near-neighbor history variables. Bootstrap comparisons indicate that this small difference is not statistically significant. Calibration diagnostics further confirm that fitted scores should be interpreted as discriminative anomaly-state indicators under the case–control design rather than as calibrated operational anomaly probabilities.