Onboard Automated CME Detection Algorithm for Visible Emission Line Coronagraph on ADITYA-L1

TL;DR

This paper presents an onboard automated CME detection algorithm for the VELC instrument on India's ADITYA-L1 mission, enabling efficient data handling and improved CME study capabilities.

Contribution

The paper introduces a novel intensity and area thresholding-based CME detection algorithm tailored for VELC's high-resolution, high-cadence solar coronagraph data, optimized for onboard implementation.

Findings

Telemetry can be reduced by over 85% while maintaining a CME detection rate above 70%.

The algorithm performs effectively on synthetic and existing coronagraph data.

Onboard detection enables higher resolution imaging with less data transmission.

Abstract

ADITYA-L1 is India's first space mission to study the Sun from Lagrangian 1 position. { \textit{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 ($\approx$ 2.51 arcsec pixel$^{-1}$) images of corona from 1.05 R$_{\odot}$ -- 3 R$_{\odot}$ at high cadence of 1 s in 10 \AA\ passband centered at 5000 \AA. 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 space- and ground-based coronagraph. Since, no existing space-based coronagraph has FOV similar to VELC, we have created synthetic coronal images for VELC FOV after including photon noise and injected different type of CMEs. 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.