The Ratan Active Region Patches (RARPs) Database: A New Database of Solar Active Region Radio Signatures from the RATAN-600 Telescope

TL;DR

The RARPs database offers a standardized, extensive collection of solar active region radio spectra from RATAN-600, enabling improved flare forecasting through machine learning and complementing magnetic field data.

Contribution

This paper introduces the first standardized, publicly accessible radio spectra database for solar active regions, facilitating advanced analysis and flare prediction.

Findings

Radio spectra contain predictive information for solar flares.

Machine learning using radio spectra can forecast flares with competitive accuracy.

Radio data improves flare prediction for M-class and above events.

Abstract

Solar flares and coronal mass ejections, originating from solar active regions (ARs), are the primary drivers of space weather and can disrupt technological systems. Forecasting efforts heavily rely on photospheric magnetic field data from the Space-weather HMI Active Region Patch (SHARPs) data products. However, the crucial energy release occurs higher in the solar corona. Radio observations from instruments like the RATAN-600 telescope directly probe this region, but their scientific use has been hindered by a lack of standardized and accessible data products. To address this gap, we have developed the Ratan Active Region Patches (RARPs) database, a new public resource of multi-frequency radio spectra for solar ARs. Generated using RATANSunPy software, RARPs provides the first standardized radio counterpart to magnetic field archives. The database contains over 160,000 calibrated AR observations from 2009 to 2025, each including 3-18 GHz spectra and rich metadata. We demonstrate the scientific utility of this database by using machine learning to forecast solar flares. The radio spectra are first compressed into low-dimensional embedded features using an autoencoder, which are then used as predictors in baseline logistic regression classifiers. We compare the predictive power of these embedded RARPs features with that of the 18 SHARPs magnetic field parameters provided in the SHARPs data product headers. Our results show that while SHARPs data provides superior flare discrimination, the radio signatures in RARPs possess clear predictive potential and, for M-class and above flares, yield lower Brier Scores and positive Brier Skill Scores relative to SHARPs, indicating more accurate probabilistic forecasts for these events. This establishes radio data as a valuable and complementary information source.