Speaker
Description
The advancement of heliophysics research and data-driven space weather studies depends critically on the availability of large-scale, standardized, and physically validated solar observational datasets, yet such resources remain scarce for the historically significant Kodaikanal Solar Observatory (KSO) archives, which span over a century of continuous solar monitoring. This work addresses that gap by presenting an automated multi-wavelength analysis pipeline that transforms KSO's historical observations into standardized, science-ready datasets, enabling systematic investigations of solar activity and long-term solar variability.
The pipeline processes high-resolution (4096 × 4096) Ca II K spectroheliograms to detect and characterize chromospheric plage regions using adaptive thresholding, edge detection, and contour-based segmentation. Accurate solar disk localization via circle-fitting algorithms and orientation correction ensures consistent spatial referencing across the full observational record. The methodology is extended to white-light and H-alpha data for automated detection of photospheric sunspots and chromospheric filaments, providing a unified multi-layer framework covering more than 100 years of daily solar observations. The framework enables consistent characterization of solar activity features across one of the world's longest continuous ground-based solar archives. Heliographic coordinates and physical areas are computed for all detected features after applying limb-darkening and foreshortening corrections, yielding reproducible measurements across wavelengths.
The pipeline outputs standardized FITS-format science products containing feature identifiers, heliographic locations, timestamps, and area measurements, validated through comparison of computed feature areas against reference measurements and manual inspection of detected plage boundaries across representative observations. These datasets facilitate reproducible analysis, solar feature studies, activity cycle characterization, and integration into broader heliophysics research workflows, while remaining suitable for future machine learning applications in solar physics and space weather research. By transforming one of the world's longest solar observational records into standardized and reproducible science-ready datasets through open data standards and automated processing pipelines, this work directly supports international capacity building in space weather science (SDG 4, SDG 9) and contributes to the data-sharing infrastructure essential for global research-to-operations transitions (SDG 17). The framework demonstrates a scalable model for integrating archival solar observations from observatories in developing nations into modern heliophysics data systems and space weather research infrastructures.
KEYWORDS: Kodaikanal Solar Observatory, Solar Feature Detection, Ground-Based Solar Data, Space Weather