Machine Learning Based Identification of Solar Disk and Plages in Kodaikanal Solar Observatory Historical Suncharts

TL;DR

This study employs convolutional neural networks to automatically identify solar disks and plages in over a century of hand-drawn suncharts from Kodaikanal Solar Observatory, enabling historical data analysis and filling observational gaps.

Contribution

First application of machine learning to extract and analyze features from Kodaikanal's historical suncharts, improving data retrieval and continuity for solar studies.

Findings

CNN accurately identifies solar disks and plages in historical suncharts.

Plage areas from suncharts agree well with Ca II K observations.

Method enables filling data gaps in long-term solar activity records.

Abstract

Kodaikanal Solar Observatory (KoSO) is one of the oldest solar observatories, possessing an archive of multi-wavelength solar observations, including white light, Ca II K, and H-alpha images spanning over a century. In addition to these observations, KoSO has preserved hand-drawn suncharts (1904-2022), on which various solar features such as sunspots, plages, filaments, and prominences are marked on the Stonyhurst grid with distinct colour coding. In this study, we present the first comprehensive result that includes the entire data set from these suncharts using a supervised Machine Learning model called "Convolutional Neural Networks (CNNs)", firstly to identify the solar disks from the charts (1909-2007), secondly to identify the plages, spanning 9 solar cycles (1916-2007). We train the CNN with the manually identified solar disk and plage. We first detect the solar limb and the North-South line in the suncharts, which enables the extraction of disk centre coordinates, radius, and P-angle. Following that, we use a CNN similar architecture to achieve accurate image segmentation for the identification of plages. We compare plage areas derived from the suncharts with those obtained from Ca II K full-disk observations, and find good agreement that demonstrates the potential application of such an ML technique for historical data. The results of this study further demonstrate the potential application of sunchart data to fill the existing data gaps in the KoSO multi-wavelength observations and contribute toward constructing a composite series over the last century.