1. Automatic detection of CMEs in STEREO-HI data Luciano Rodriguez, Sarah Willems, Vaibhav Pant, Marilena Mierla, Andy Devos and the HELCATS team ESWW12, 24 th November 2015

2. HELCATS EU FP7 project (2014-2017) http://www.helcats-fp7.eu Create CME and CIR catalogues, mainly based on STEREO-Hi data (but not only), analyse their kinematic properties and initialise models based on these properties. Poster (Plotnikov et al., Session 8, Wed) and a fair stand on Wed

3. Automatic CME catalogue: Application of CACTus on STEREO/HI1

4. http://sidc.oma.be/cactus/

5. Different geometry CMEs are fainter Include planets and stars Lower cadence

6. Preprocessing After some cleaningL2 images (1-day backgrounds removed)

7. Conversion to polar coordinates Angle from solar north Projected distance from Sun (100,000 km/px)

8. r-t slices Extraction of r – t slices for each angle. t r r t [r, t] for each θ

9. CME extraction Detected ridgesr-t slice t-v map Original data Filtered data CMEs are seen in r-t slices as bright ridges by using the Hough transform. r-t map Detected ridges

10. CACTus output # CME | t0 | pa | da | NoPA| SuPA| v | dv | minv| maxv 0006|2010/04/03 12:09| 0102| 072| 0066| 0138| 0823| 0110| 0571| 1041 Position and width Speed Starting time

12. The catalogue

13. Manual vs. Automatic

14. Position angle CME PA Histogram density CME PA (manual) CME PA (auto)

15. Angular width CME AW (manual) CME AW Histogram density CME AW (auto)

16. Speeds Histogram density CME speed (km/s) CME speed (manual) CME speed (auto)

17. Automatic CME rate vs. sunspot number

18. Work on progress and future Comparison of manual and automatic catalogues Initial statistical analysys Visual inspection of events Analysis of individual cases Comparison with other catalogues Real time catalogue of CACTus for HI: CACThi !

19. Extra material

21. CACTus output: Visualisation After some thresholding and clustering we obtain the final detection map in which each color indicates a different CME.