Analysis Of Signal To Noise Ratio In Coronagraph Observations Of Coronal Mass Ejections

We establish a baseline signal-to-noise ratio (SNR) requirement for the European Space Agency (ESA)-funded Solar Coronagraph for OPErations (SCOPE) instrument in its field of view of 2.5-30 solar radii based on existing observations by the Solar and Heliospheric Observatory (SOHO). Using automatic detection of coronal mass ejections (CMEs), we anaylse the impacts when SNR deviates significantly from our previously established baseline. For our analysis, SNR values are estimated from observations made by the C3 coronagraph on the Solar and Heliospheric Observatory (SOHO) spacecraft for a number of different CMEs. Additionally, we generate a series of artificial coronagraph images, each consisting of a modelled coronal background and a CME, the latter simulated using the graduated cylindrical shell (GCS) model together with the SCRaytrace code available in the Interactive Data Language (IDL) SolarSoft library. Images are created with CME SNR levels between 0.5 and 10 at the outer edge of the field of view (FOV), generated by adding Poisson noise, and velocities between 700 km s(-1) and 2800 km s(-1). The images are analysed for the detectability of the CME above the noise with the automatic CME detection tool CACTus. We find in the analysed C3 images that CMEs near the outer edge of the field of view are typically 2% of the total brightness and have an SNR between 1 and 4 at their leading edge. An SNR of 4 is defined as the baseline SNR for SCOPE. The automated detection of CMEs in our simulated images by CACTus succeeded well down to SNR = 1 and for CME velocities up to 1400 km s(-1). At lower SNR and higher velocity of >= 2100 km s(-1) the detection started to break down. For SCOPE, the results from the two approaches confirm that the initial design goal of SNR = 4 would, if achieved, deliver a comparable performance to established data used in operations today, with a more compact instrument design, and a margin in SNR before existing automatic detection produces significant false positives.