Detection of Solar Filaments using Suncharts from Kodaikanal Solar Observatory Archive Employing a Clustering Approach

TL;DR

This study digitized and analyzed over a century of solar suncharts from Kodaikanal Observatory using clustering to detect filaments, revealing their properties and migration patterns, and validating results with photographic data.

Contribution

Introduces a novel method employing k-means clustering to detect and analyze solar filaments from historical hand-drawn suncharts, enabling long-term solar feature studies.

Findings

Filament length and area increase with latitude.

Pole-ward migration is dominated by a specific tilt sign.

Good agreement with photographic plate data.

Abstract

With over 100 years of solar observations, the Kodaikanal Solar Observatory (KoSO) is a one-of-a-kind solar data repository in the world. Among its many data catalogues, the `suncharts' at KoSO are of particular interest. These Suncharts (1904-2020) are coloured drawings of different solar features, such as sunspots, plages, filaments, and prominences, made on papers with a Stonyhurst latitude-longitude grid etched on them. In this paper, we analyze this unique data by first digitizing each suncharts using an industry-standard scanner and saving those digital images in high-resolution `.tif' format. We then examine the Cycle~19 and Cycle~20 data (two of the strongest cycles of the last century) with the aim of detecting filaments. To this end, we employed `k-means clustering' method and obtained different filament parameters such as position, tilt angle, length, and area. Our results show that filament length (and area) increases with latitude and the pole-ward migration is clearly dominated by a particular tilt sign. Lastly, we cross-verified our findings with results from KoSO digitized photographic plate database for the overlapping time period and obtained a good agreement between them. This work, acting as a proof-of-the-concept, will kick-start new efforts to effectively use the entire hand-drawn series of multi-feature, full-disk solar data and enable researchers to extract new sciences, such as the generation of pseudo magnetograms for the last 100 years.