We present SOPRA (Solar Off-limb Prominence Reconstruction Algorithm), an algorithm which automatically detects prominences above the limb in EUV images taken in the He II channel at 304 A and subsequently reconstructs the structures to extract their morphological parameters. SOPRA determines the characteristics of radial intensity profiles outward from the limb and uses Support Vector Machines in order to classify them as belonging to prominence or other structures. Pixels detected as belonging to a prominence are then used as the starting point to reconstruct the whole object by morphological image processing techniques. The algorithm is applied to the entire SOHO/EIT data set and a catalogue of detected prominences is produced. We present the initial statistical analysis of this catalogue, and discuss its use for solar prominence research and for space weather monitoring. We also assess the performance of SOPRA when applied to SDO/AIA images.

1. A new solar prominence
catalogue with SOPRA
Nicolas Labrosse1, Silvia Dalla2,
Jinchang Ren3, and Steve Marshall3
1- University of Glasgow, Scotland
2- University of Central Lancashire, England
3- University of Strathclyde, Scotland
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2. Aims
• Fact: some attempts to make large statistical studies of
global prominence properties
– Manual detection
 E.g.
Gilbert et al (2000): identify distinguishing characteristics of APs and
EPs and study the relationship between prominence activity and CMEs
– Automatic detection
 Foullon & Verwichte (2005); Wang et al. (2010)
• Can be improved by producing large catalogues for
statistical studies
• Link with filament / flare / CME catalogues
• Automatic feature recognition from SDO/AIA data
13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011 1

3. SOPRA
• What does SOPRA mean?
• sopra prep
• a (gen) over
• b (più in su di) above
Solar Off-limb Prominence Reconstruction Algorithm
• Approach
– Process only He II 304 images
 Prominences are best viewed in this channel
 Limits dependence on other channels' availability
 Faster to process than when using additional channels
• Now developed for SOHO/EIT data
• Being tested on SDO/AIA
13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011 2

4. Method
• Pre-processing of image
• Take radial intensity profiles,
Training of classifier
calculate moments and label them
• Train Support Vector Machine
• Algorithm recognises off-limb structures
based on the moments of the radial Classification
intensity profiles
• At the position on the limb where a
prominence is detected, morphological Reconstruction
image reconstruction is applied
• Prominence characteristics are extracted
– Results feed the catalogue
13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011 3

5. Method
• Pre-processing of image
• Take radial intensity profiles,
calculate moments and label them
• Train Support Vector Machine
• Algorithm recognises off-limb structures quiet corona
based on the moments of the radialregion
prominence
active Classification
intensity profiles
• At the position on the limb where a
prominence is detected, morphological Reconstruction
image reconstruction is applied
• Prominence characteristics are extracted
– Results feed the catalogue
13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011 4

6. Method
• Pre-processing of image
• Take radial intensity profiles,
calculate moments and label them
• Train Support Vector Machine
• Algorithm recognises off-limb structures
based on the moments of the radial
intensity profiles
• At the position on the limb where a
prominence is detected, morphological
image reconstruction is applied
• Prominence characteristics are extracted
– Results feed the catalogue
13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011 5

7. Method
• Pre-processing of image
• Take radial intensity profiles,
calculate moments and label them
• Train Support Vector Machine
• Algorithm recognises off-limb structures
based on the moments of the radial
intensity profiles
• At the position on the limb where a
prominence is detected, morphological
image reconstruction is applied
• Prominence characteristics are extracted
– Results feed the catalogue
13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011 6

8. Applying SOPRA on the EIT database
• We processed 29367 FITS files covering ~ all EIT full-disk
images at 304 Å between 01/1996 and 06/2011
– Took > 2 weeks on 4x Quad Core AMD with 64 GB of RAM
• 315307 unique prominences detected
13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011 7

9. Results: histograms
Prominence area Time
Latitude
Altitude
13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011 8

10. Results: correlations
Latitude vs time
Area vs time
Altitude vs time Altitude vs latitude
13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011 9

11. Conclusions
• SOPRA: Solar Off-limb Prominence Reconstruction Algorithm
– Labrosse et al., Solar Physics, 262, 449 (2010)
• The algorithm as a whole is working well
– Production of catalogue from EIT observations since 1996
– Generates HEK-compliant output
– Large samples for statistical studies (> 300000 detections so far)
– Track prominence eruptions
– Link with filament / flare / CME catalogues
– Monitor impact of prominence eruptions on space weather
Thank you for listening to this last talk!
13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011 10

12. AIA
13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011 11