Onboard Automated CME Detection Algorithm for the Visible Emission Line Coronagraph on <Emphasis Type="Italic">ADITYA-L1</Emphasis> |
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| Authors: | Ritesh Patel " target="_blank">Amareswari K Vaibhav Pant " target="_blank">Dipankar Banerjee Sankarasubramanian K Amit Kumar |
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| Institution: | 1.Indian Institute of Astrophysics,Bangalore,India;2.ISRO Satellite Centre,Bangalore,India;3.Center of Excellence in Space Sciences,IISER Kolkata,Kolkata,India;4.Center for Mathematical Plasma Astrophysics,KU Leuven,Leuven,Belgium |
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| Abstract: | ADITYA-L1 is India’s first space mission to study the Sun from the Lagrange 1 position. The Visible Emission Line Coronagraph (VELC) is one of seven payloads on the ADITYA-L1 mission, which is 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 (\({\approx}\,2.51~\mbox{arcsec}\,\mbox{pixel}^{-1}\)) images of the corona from \(1.05~\mbox{R}_{\odot}\,\mbox{--}\,3~\mbox{R}_{\odot}\) at a high cadence of 1 s in the 10 Å passband centered at 5000 Å. Because telemetry at the Lagrangian 1 position is limited, we plan to implement an onboard automated CME detection algorithm. The detection algorithm is based on intensity thresholding followed by area thresholding in successive difference images that are spatially rebinned to improve the 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 a space-based coronagraph, and K-Cor, a ground-based coronagraph, because they have a field of view (FOV) that is most similar to that of VELC. Since no existing space-based coronagraph has a FOV similar to VELC, we have created synthetic coronal images for the VELC FOV after including photon noise and injected CMEs of different types. The performance of the CME detection algorithm was tested on these images. We found that for VELC images, the telemetry can be reduced by a factor of 85% or more while maintaining a CME detection rate of 70% or higher at the same time. Finally, we discuss the advantages and disadvantages of this algorithm. The application of such an onboard algorithm in future will enable us to take higher resolution images with an improved cadence from space and simultaneously reduce the load on limited telemetry. This will help understanding CMEs better by studying their characteristics with improved spatial and temporal resolution. |
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