Validation of CME Detection Software (CACTus) by Means of Simulated Data, and Analysis of Projection Effects on CME Velocity Measurements

Abstract

In the context of space weather forecasting, an automated detection of coronal mass ejections (CMEs) becomes more and more important for efficiently handling a large data flow which is expected from recently-launched and future solar missions. In this paper we validate the detection software package "CACTus" by applying the program to synthetic data from our 3D time-dependent CME simulations instead of observational data. The main strength of this study is that we know in advance what should be detected. We describe the sensitivities and strengths of automated detection, more specific for the CACTus program, resulting in a better understanding of CME detection on one hand and the calibration of the CACTus software on the other hand, suggesting possible improvements of the package. In addition, the simulation is an ideal tool to investigate projection effects on CME velocity measurements.

Multiwavelength Observations of a Slow-Rise, Multistep X1.6 Flare and the Associated Eruption

“…Figure 14 shows that the X1.6 flare was followed by a moderately fast coronal mass ejection (CME), which was first detected in the SOHO/LASCO C2 coronagraph at 18:08 UT. According to the CACTus CME Catalog 1 (Bonte et al 2011) the speed of the CME ranged from 224 km s −1 to 2016 km s −1 depending on the position angle, with the average value of 938 km s −1 . The SEEDS 2 catalogue (Olmedo et al 2008) reported an average value of ∼505 km s −1 with a negative acceleration of ∼122 m s −2 .…”

Nowcasting of Solar Energetic Particle Events using near real-time Coronal Mass Ejection characteristics in the framework of the FORSPEF tool

“…It is designed to detect CMEs in coronagraph images, thus the input to the CACTus software is a time sequence of observational data obtained from both the LASCO C2 and C3 coronagraphs. A typical output of CACTus shows the detected events in a synoptic diagram, including the characteristics per event like the onset time, the principle angle (the projection of the principle direction of an event on the field-of-view), the angular width, and the velocity estimation (Bonte et al, 2011).…”

Detection for coronal mass ejection based on Gaussian Mixture Models

“…(2) 基于计算机图像处理的CME自动探测方 法 . 第 1个基于计算机自动检测CME的方法 [11,12] 在 2002年被首次提出, 开发了软件CACTus (Computer Aided CME Tracking Software Package, http://sidc. oma.be/cactus/catalog.php), 这 个 方 法 是 利 用 基 于 极 坐 标 下 的 霍 夫 变 换 技 术 对 观 测 到 的 日 冕 图 像 进 行 CME检测; 2005年, Boursier等人 [13~15] 提出了ARTEMIS 方法, 这种方法通过将LASCO C2日冕图像转换到 Synoptic Maps综合图中, 利用垂直条纹来检测CME;…”