Multi-Thermal CME Detection with ALMANAC

Abstract

Reliable identification of low-coronal CME origins remains a key limitation in space weather forecasting with coronagraphs not directly resolving low-coronal signatures. We present a re-engineered multi-thermal implementation of the ALMANAC algorithm, designed to detect eruptive signatures in EUV observations. The framework extends the method to a multi-wavelength system, improving robustness against projection effects, instrumental artifacts, and wavelength-dependent ambiguities via complementary temperature responses. A spatiotemporal clustering scheme merges detections across channels, reducing event bifurcation and improving coherence while maintaining NRT performance through parallel computing. Benchmarking against 20 halo CMEs from CDAW shows improved interpretability and operational usability, with clearer separation of eruptions and more consistent onset localisation relative to coronagraph estimates. The main benefits arise from improved event discrimination, reduced fragmentation, and more interpretable source region identification. ALMANAC shows sensitivity to precursor low-coronal activity not always captured in white-light catalogues, highlighting its advantages for early warning detection. When coupled with the ARTop framework, it enables co-analysis of coronal intensity variability and photospheric magnetic evolution. In this context, kurtosis time-series from multi-wavelength EUV data exhibit recurrent pre-eruptive spikes that frequently align with enhancements in magnetic winding and helicity injection. Across multiple regions, these signatures often precede solar activity, including potential discrimination of X-class flares, while remaining suppressed during magnetically quiet intervals. Overall, integrating coronal diagnostics with photospheric topology offers a pathway toward improved eruption forecasting and space weather prediction.