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Context. Sunspots have been observed since Galileo Galilei invented the telescope. Later, sunspot drawings have been upgraded to image storage using photographic plate in the second half of nineteenth century. These photographic images are valuable data resources for studying long-term changes in the solar magnetic field and its influence on the Earth's climate and weather. Aims. Digitized photographic plates cannot be used directly for the scientific analysis. It requires certain steps of calibration and processing before using them for extracting any useful information. The final data can be used to study solar cycle variations over several cycles. Methods. We digitized more than 100 years of white-light images stored in photographic plates and films that are available at Kodaikanal observatory starting from 1904. The images were digitized using a 4k × 4k format CCD-camera-based digitizer unit.The digitized images were calibrated for relative plate density and aligned in such a way that the solar north is in upward direction. A semi-automated sunspot detection technique was used to identify the sunspots on the digitized images.Results. In addition to describing the calibration procedure and availability of the data, we here present preliminary results on the sunspot area measurements and their variation with time. The results show that the white-light images have a uniform spatial resolution throughout the 90 years of observations. However, the contrast of the images decreases from 1968 onwards. The images are circular and do not show any major geometrical distortions. The measured monthly averaged sunspot areas closely match the Greenwich sunspot area over the four solar cycles studied here. The yearly averaged sunspot area shows a high degree of correlation with the Greenwich sunspot area. Though the monthly averaged sunspot number shows a good correlation with the monthly averaged sunspot areas, there is a slight anti-correlation between the two during solar maximum. Conclusions. The Kodaikanal data archive is hosted at http://kso.iiap.res.in. The long time sequence of the Kodaikanal whitelight images provides a consistent data set for sunspot areas and other proxies. Many studies can be performed using Kodaikanal data alone without requiring intercalibration between different data sources.

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ADITYA-L1 is India's first space mission to study the Sun from Lagrangian 1 position. Visible Emission Line Coronagraph (VELC) is one of the seven payloads in ADITYA-L1 mission scheduled to be launched around 2020. One of the primary objectives of the VELC is to study the dynamics of coronal mass ejections (CMEs) in the inner corona. This will be accomplished by taking high resolution (≈ 2.51 arcsec pixel −1 ) images of corona from 1.05 R -3 R at high cadence of 1 s in 10Å passband centered at 5000Å. Due to limited telemetry at Lagrangian 1 position we plan to implement an onboard automated CME detection algorithm. The detection algorithm is based on the intensity thresholding followed by the area thresholding in successive difference images spatially re-binned to improve signal to noise ratio. We present the results of the application of this algorithm on the data from existing coronagraphs such as STEREO/SECCHI COR-1, which is space based coronagraph, and K-Cor, a ground based coronagraph, because they have field of view (FOV) nearest to VELC. Since, no existing space-based coronagraph has FOV similar to VELC, we have created synthetic coronal images for VELC FOV after including photon R. Patel ritesh.patel@iiap.res.in V. Pant vaibhav@iiap.res.in Amareswari K. Patel R. et al.noise and injected CMEs of different types. The performance of CME detection algorithm is tested on these images. We found that for VELC images, the telemetry can be reduced by a factor of 85% or more keeping CME detection rate of 70% or above at the same time. Finally, we discuss the advantages and disadvantages of this algorithm. The application of such onboard algorithm in future will enable us to take higher resolution images with improved cadence from space and also reduce the load on limited telemetry at the same time. This will help in better understanding of CMEs by studying their characteristics with improved spatial and temporal resolutions.

An onboard automated coronal mass ejections (CMEs) detection algorithm has been developed for Visible Emission Line Coronagraph (VELC) onboard ADITYA-L1. The aim of this algorithm is to reduce the load on telemetry by sending the high spatial (~ 2.51 arcsec pixel−1) and temporal (1 s) resolution images of corona from 1.05 R⊙ to 3 R⊙, containing CMEs and rejecting others. It is based on intensity thresholding followed by an area thresholding in successive running difference images which are re-binned to lower resolution to improve signal to noise. Here we present the results of application of the algorithm on synthetic corona images generated for the VELC field of view (FOV).