Detection of Solar Plasma Instabilities Using Multi-Channel GOES Observations: Toward Early Solar Flare Forecasting

Solar flares are explosive releases of magnetic energy in the solar corona that can significantly affect space weather conditions near Earth. Reliable detection of early flare precursors remains an important challenge for operational forecasting. We investigate whether statistical signatures in multi-channel GOES observations provide early indicators of flare activity. We consider soft X-ray flux, a flare event catalogue, and complementary channels (e.g. magnetometer proxies when available). We quantify pre-event variability via windowed variance and propose a composite instability indicator that integrates X-ray variability and magnetic perturbation surrogates. In addition, we outline an operator-based, non-equilibrium interpretation in which precursor build-up corresponds to a structured increase of temporal memory and phase-like degrees of freedom in the observed signals. If confirmed over larger samples, such signatures could improve warning lead times for satellite operators and critical infrastructure affected by severe space weather.