Guillem Castelló, Manuel Luna, Jaume Terradas

Solar filament oscillations provide important diagnostics of prominence magnetic structure and stability, but their detection in long Hαarchives has traditionally relied on visual inspection, manually placed slits, and time--distance diagrams. We present an automatic pipeline for detecting spatially coherent filament oscillations in GONG Hαimage sequences. The method combines image preprocessing and coalignment, deep-learning-based filament detection and segmentation, multi-scale spatial averaging, Lomb--Scargle spectral analysis, convolutional-neural-network background estimation, empirical calibration of significance thresholds, and clustering of candidate detections in period and space. Only oscillations supported across at least four spatial scales are retained, reducing sensitivity to local pixel-scale intensity fluctuations. The pipeline recovers several events from the manual GONG catalog of Luna et al. (2018), including the 1 January 2014 oscillation with a period of approximately 76 min. Applied to the first two weeks of January 2014, it identifies 91 oscillatory events, compared with 22 non-duplicate events in the corresponding manual catalog, with detected periods ranging from about 20 to 126 min. It also detects previously unreported oscillations, including an event on 13 January 2014 with a period of approximately 86 min that is independently confirmed using a conventional time--distance diagram. These results demonstrate that automated filament segmentation, multi-scale spectral analysis, and calibrated significance testing can provide a reproducible and scalable alternative to manual slit-based searches. The pipeline substantially increases detection sensitivity while preferentially selecting coherent filament-scale oscillations, enabling future statistical studies over extended GONG intervals and across the solar cycle.