Predicting Solar Magnetic Activity from S ph and Seismic Parameters Using Random Forest Regression

We investigate the potential of using the photometric magnetic proxy Sph and seismic parameters, such as the frequency of maximum power ( ) and the large frequency separation (Δν), derived from Solar and Heliospheric Observatory/Variability of Solar Irradiance and Gravity Oscillations observations to predict the 10.7 cm solar radio flux, a widely used index of solar magnetic activity. A random forest regression model is trained and tested on time series divided into multiple temporal subsets and input parameter combinations. The model achieves strong predictive performance (R2 > 0.92) across configurations and significantly outperforms a classical linear regression model. Our results show that Sph effectively captures long-term variations, while the seismic amplitude parameter is more responsive to short-term fluctuations. Combining Sph with the full set of seismic parameters yields the highest accuracy and offers a promising approach for diagnosing activity in other solar-like stars where direct magnetic field measurements are infeasible.