a new solar prominence catalogue with sopra
DESCRIPTION
We present SOPRA (Solar Off-limb Prominence Reconstruction Algorithm), an algorithmwhich automatically detects prominences above the limb in EUV images taken in the He IIchannel 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 anduses Support Vector Machines in order to classify them as belonging to prominence or otherstructures. Pixels detected as belonging to a prominence are then used as the starting pointto reconstruct the whole object by morphological image processing techniques.The algorithm is applied to the entire SOHO/EIT data set and a catalogue of detectedprominences is produced. We present the initial statistical analysis of this catalogue, anddiscuss its use for solar prominence research and for space weather monitoring.We also assess the performance of SOPRA when applied to SDO/AIA images.TRANSCRIPT
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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13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011
• 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
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Aims
13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011
• 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
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SOPRA
13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011
• 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
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Method
Training of classifier
Classification
Reconstruction
13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011
• 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
4
Method
Classification
Reconstruction
prominence
active region quiet corona
13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011
• 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
5
Method
13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011
• 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
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Method
13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011
• 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
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Applying SOPRA on the EIT database
13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011 8
Results: histograms
Prominence area
Latitude
Time
Altitude
13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011 9
Results: correlations
Area vs time
Altitude vs time
Latitude vs time
Altitude vs latitude
13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011
• 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!
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Conclusions
13th European Solar Physics Meeting, Rhodes, Greece, 12-16 September 2011 – Nicolas Labrosse – 16/9/2011 11
AIA