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

We investigate the potential of using the photometric magnetic proxy S_ph and seismic parameters, such as the frequency of maximum power (ν_max) 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 S_ph effectively captures long-term variations, while the seismic amplitude parameter H max is more responsive to short-term fluctuations. Combining S_ph 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.