RatanSunPy: A robust preprocessing pipeline for RATAN-600 solar radio observations data
I. Knyazeva, I. Lysov, E. Kurochkin, A. Shendrik, D. Derkach, N. Makarenko
TL;DR
This paper introduces RatanSunPy, an open-source Python package designed to streamline the preprocessing and analysis of RATAN-600 solar radio data, addressing the complexity of traditional workflows. The approach combines data access, calibration against quiet Sun templates, automatic local-source detection, and cross-referencing with NOAA SWPC active region data, augmented by Gaussian analysis to extract AR properties across 3–18 GHz. Key contributions include a SunPy-like, extensible software architecture with dedicated RATAN-600 data clients, automated QA via pytest, and practical usage examples that enable end-to-end processing within a single environment. The work enables more efficient studies of solar atmosphere dynamics and supports improved space weather diagnostics through integrated, cross-instrument capabilities and potential cloud-based analysis.
Abstract
The advancement of observational technologies and software for processing and visualizing spectro-polarimetric microwave data obtained with the RATAN-600 radio telescope opens new opportunities for studying the physical characteristics of solar plasma at the levels of the chromosphere and corona. These levels remain some difficult to detect in the ultraviolet and X-ray ranges. The development of such methods allows for more precise investigation of the fine structure and dynamics of the solar atmosphere, thereby deepening our understanding of the processes occurring in these layers. The obtained data also can be utilized for diagnosing solar plasma and forecasting solar activity. However, using RATAN-600 data requires extensive data processing and familiarity with the RATAN-600. This paper introduces RatanSunPy, an open-source Python package developed for accessing, visualizing, and analyzing multi-band radio observations of the Sun from the RATAN-600 solar complex. The package offers comprehensive data processing functionalities, including direct access to raw data, essential processing steps such as calibration and quiet Sun normalization, and tools for analyzing solar activity. This includes automatic detection of local sources, identifying them with NOAA (National Oceanic and Atmospheric Administration) active regions, and further determining parameters for local sources and active regions. By streamlining data processing workflows, RatanSunPy enables researchers to investigate the fine structure and dynamics of the solar atmosphere more efficiently, contributing to advancements in solar physics and space weather forecasting.