Aditya Priyadarshi

Kodaikanal Solar Observatory (KoSO) is one of the oldest solar observatories, possessing an archive of multiwavelength solar observations, including white light, Ca ii K, and H α 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 color coding. In this study, we present the first comprehensive result that includes the entire dataset from these suncharts using a supervised machine learning (ML) model called “convolutional neural networks” (CNNs), first to identify the solar disks from the charts (1909–2007) and second to identify the plages, spanning nine solar cycles (1916–2007). We train the CNN with the manually identified solar disk and plages. We first detect the solar limb and the north–south line in the suncharts, which enables the extraction of disk center 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 multiwavelength observations and contribute toward constructing a composite series over the last century.

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.

With over 100 yr of solar observations, the Kodaikanal Solar Observatory (KoSO) is a one-of-a-kind solar data repository in the world. Among its many data catalogs, the “suncharts” at KoSO are of particular interest. These suncharts (1904–2020) are colored 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 sunchart using an industry-standard scanner and saving those digital images in a high-resolution “.tif” format. We then examine 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 the “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 poleward 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 kickstart new efforts to effectively use the entire hand-drawn series of multifeature, full-disk solar data and enable researchers to extract new sciences, such as the generation of pseudomagnetograms for the last 100 yr.

As violations in the city are on the rise, we bring out a citizen-driven application to allow them to take responsibility for being a lawful Samaritan was the need of the hour. The Bangalore city Traffic Police has released an application ‘Public Eye’ which only partially serves this purpose. In this project, we attempt to contribute to this good initiative by enhancing the User Experience and adding the power of Machine Learning and Artificial Intelligence to simplify violation detection, while addressing privacy concerns. In our work, we have explored state-of-the-art models available for the task of detection and recognition in images. After studying the needs of users meticulously, we built an effective and meaningful User Experience in the App. The strengths of this App are its ease of use, flexibility, and privacy amongst others for an effective traffic violation reporting. Our approach to gamifying the experience will attract more users to take responsibility of being law abiding citizen. The efficacy of its usage is validated through volunteers.