Automated detection of Coronal Mass Ejections in Visible Emission Line Coronagraph (VELC) on-board ADITYA-L1

TL;DR

This paper presents an automated algorithm for detecting coronal mass ejections in images from the VELC instrument on ADITYA-L1, aiming to reduce data load by selectively transmitting CME-containing images.

Contribution

The paper introduces a novel onboard CME detection algorithm based on intensity and area thresholding in difference images, optimized for VELC's high-resolution solar corona observations.

Findings

Algorithm successfully detects CMEs in synthetic images.

Reduces data transmission by filtering non-CME images.

Demonstrates effectiveness in simulated conditions.

Abstract

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 ($\sim$ 2.51 arcsec pixel$^{-1}$) and temporal (1 s) resolution images of corona from 1.05 R$_{\odot}$ to 3 R$_{\odot}$, 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).