Frequency–Time Harmonization of Multi-Station Spectrograms for Machine Learning Applications in Solar Radio Burst Analysis

The collection of solar radio burst spectrograms through the spatial distribution of survey sites reveals heterogeneity and differences in frequency sampling, temporal resolution, signal intensity, and local observing conditions. Because of this heterogeneity, it is difficult to incorporate observations of diverse stations directly into a single dataset, which restricts the generalizability of machine learning models that were trained using records from isolated stations. Thus, it is well established that for reliable automated analysis of solar radio bursts, a standard representation of spectrograms is necessary. This paper proposes a framework for harmonizing heterogeneous multi-site spectrograms of solar radio bursts, which transforms them into a unified representation capable of accommodating station-specific observational configurations suitable for machine learning applications. The proposed methodology combines adaptive frequency interval selection, frequency sampling consistency checking, logarithmic intensity transformation with baseline correction, time–frequency harmonization, and explicit handling of missing frequency channels using binary validity masks. To overcome the heterogeneous nature of the observations taken from different instruments and stations, nearest-neighbor interpolation in both time and frequency domains is applied. The unified frequency grid is constructed considering the statistics of the frequency channels used in spectrometers that form the e-CALLISTO network, a space weather instrument array. Two one-dimensional convolutional autoencoders with two latent space dimensions (32 and 64) are proposed and trained on more than 17000 spectrograms to evaluate the quality of latent representations of the harmonized data. The best performance was achieved by the autoencoder with simpler architecture and with 64-dimensional latent space, reaching root-mean-square error of 0.88dB and a mean absolute error of 0.61dB. The experimental results show that models with 64-dimensional latent space outperform their 32-dimensional counterparts, while increased architectural complexity provided no substantial improvement. The proposed methodology provides a standardized basis for constructing harmonized datasets suitable for a wide range of subsequent machine learning applications in solar radio astronomy, including automated burst detection, classification, representation learning, anomaly detection, and predictive modeling.