Summary We describe an HMI data product consisting of tracked magnetic features on the scale of...
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Summary We describe an HMI data product consisting of tracked magnetic features on the scale of solar active regions, abbreviated HARPs (HMI Active Region Patches). The HARP data series should be helpful for: • Subsetting individual active regions • Development of near-real-time (NRT) space weather indices for individual active regions • Defining closed magnetic structures for computationally-intensive disambiguation of vector fields The data series builds upon the 720s cadence active- region mask data product, Marmask_720s. These image- size masks identify instantaneously-observed magnetic features. Using these masks as a starting point, large spatially-coherent regions are extracted using convolution with a longitudinally extended kernel on a spherical domain. The resulting set of identified regions is then tracked from image to image. The metric for inter- image association is area of overlap between the best current estimate of AR location, as predicted by temporally extrapolating each currently tracked object, and the set of instantaneously-observed magnetic structures. Once completed tracks have been extracted, they are made into a standardized HARP data series by finding the smallest constant-angular-velocity box, of constant width in latitude and longitude, that encompasses all appearances of the active region. Mask Overview • Bayesian approach: maximize posterior probability of an integer mask given image data (two images) As in Turmon et al. 2002; Turmon et al. 2010 • Data series is Marmask_720s, derived using M_720s and Ic_720s • Pipeline module yields 4096x4096 BITPIX=8 masks, in image coordinate frame, with WCS and links to source data. May 2010 – Mar 2011 available through JSOC About 35s to compute per mask References M. Turmon, H. Jones, J. Pap, O. Malanushenko, “Statistical feature recognition for multidimensional solar imagery”, Solar Physics, 2010. H. Jones, G. Chapman, K. Harvey, J. Pap, D. Preminger, M. Turmon, S. Walton, “A comparison of feature classification...”, Solar Physics, 2007. M. Turmon, J. Pap, S. Mukhtar, “Statistical pattern recognition for labeling solar active regions: Application to SoHO/MDI,” ApJ, 2002, 396-407. The mixture modeling work appeared in: Mixtures-2001, “Recent Developments in Mixture Modelling,” Hamburg. Compstat-2004, Prague, as “Symmetric Normal Mixtures” National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Status and Plans • Masks are usable, and routinely computed in the pipeline. We hope to separate umbra/penumbra and identify faculae (we await limb-darkening- removed Ic data series) • Tracks are not yet being computed as part of pipeline production, but we expect them by July. Remaining issues are tracker parameter tweaks and software integration details. Technical improvements: remove length-1 tracks from cosmic rays, automatically flag gross tracking errors, link to NOAA AR numbers, compute overall track quality metrics, visualize tracks in summary form. Active Region Grouping • Mask is a set of pixels; we wish to group clumps of pixels into NOAA-like ARs • Formal literature on this problem is not well- developed • We have used a matched filter approach with an elongated Gaussian kernel • Mpatch_720s data series has instantaneous patches May 2010 – Mar 2011 available through JSOC About 30s to compute per mask MDI Labeling 2002 Sep. 02, 11:11 UTC Convolved with Template Identified Groups Grouping Example Grouping Details • Gaussian in 3D pixel-pixel distance, stretched longitudinally • FWHM ~50x25Mm (~150x75 HMI pixel) at disk center • Convolution on sphere to treat the limb fairly • AR masks are sparse: allows fast convolution (HMI: 12s) Active Region Tracking • Core idea: Associate ARs in before (B) and after (A) images • Correlation-based tracker • Standard latitude-dependent motion model • Compute area-of-overlap of AR patches found above • Overlap between a in A and B in B is D(a,b) • Solve linear assignment problem to match A up to B: where P is a permutation matrix giving the B-to-A mapping Fast, exact solution by linear programming • Chaining this association across many frames yields tracks • Tracks can be finalized only after they are unseen for a given time A B Before After Track Data Characteristics • Have tracks for May 2010 – April 2011. • 12 months, 43000 masks, 563 tracks. • Median length 150 frames. 76 tracks of length 1 (cosmic rays) 28 are ≥ 12 days (1440 frames) Examples of Tracked Frames • These are flipped N-S. Apologies! • HMI frames were reduced to 1024x1024, and the data series was downsampled 120x to one frame per day. • February 2011 flaring AR is in orange. • Yellow AR resulted from several merges; some can be seen in these frames. • Numerous small ARs died after frame 1; red AR died after frame 5. Mask Algorithm • Bayesian approach trades off pixel-by-pixel agreement of mask value to the data versus spatial coherence of labels Limited usage of spatial information to disambiguate cases near the class boundary. Smoothness takes spherical geometry into account, see Turmon et al. 2010 for details • Mask is found by discrete optimization of the above posterior • Current region models do not use intensity information Flattened Ic not quite ready Mask Models and Examples • Histogram (below) shows pixels (shaded gray) near critical |B| switch classes due to neighbors • Sample mask (right) shows fine spatial structure is preserved. Mask: 2011/02/14 12:00 zoom Histogram of Track Lengths Track Length (in frames) Kernel at Disk Center The HARP Data Product • HARPs are indexed by HARP number, analogous to NOAA AR number, and time (T_REC) • For a single HARP, the data series is a list of patches containing the entire disk passage of the HARP. The HARP is extrapolated backward and forward in time using differential rotation formulae so that the entire disk passage is captured, even if the HARP is not visibly active. • Each patch in the list is a rectangular cutout from the imager plane. A patch can be overlaid on a full-disk image (e.g., from M_720s) by a simple coordinate shift. • See the banner at the top of the poster for an example. • The footprint covered by the patch is determined by the the smallest lat/lon bounding box that encompasses all appearances of that particular HARP. See diagram below. • The animating idea is that the observer is hovering over the AR, staying at a constant latitude and moving at a constant angular rate in longitude. Kernel at Limb 50 Mm Example AR Mag. (for scale) Example AR Mask B (gauss) Relative Frequency Tracked Patches of Solar Activity for HMI Michael Turmon (JPL/Caltech), Xudong Sun (Stanford), Todd Hoeksema (Stanford) 1 Time S a m e l a t / l o n
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Transcript of Summary We describe an HMI data product consisting of tracked magnetic features on the scale of...
Summary
We describe an HMI data product consisting of tracked magnetic features on the scale of solar active regions, abbreviated HARPs (HMI Active Region Patches). The HARP data series should be helpful for:
• Subsetting individual active regions• Development of near-real-time (NRT) space weather indices for
individual active regions• Defining closed magnetic structures for computationally-intensive
disambiguation of vector fields
The data series builds upon the 720s cadence active-region mask data product, Marmask_720s. These image-size masks identify instantaneously-observed magnetic features. Using these masks as a starting point, large spatially-coherent regions are extracted using convolution with a longitudinally extended kernel on a spherical domain.
The resulting set of identified regions is then tracked from image to image. The metric for inter-image association is area of overlap between the best current estimate of AR location, as predicted by temporally extrapolating each currently tracked object, and the set of instantaneously-observed magnetic structures.
Once completed tracks have been extracted, they are made into a standardized HARP data series by finding the smallest constant-angular-velocity box, of constant width in latitude and longitude, that encompasses all appearances of the active region.
Mask Overview• Bayesian approach: maximize posterior probability of an integer
mask given image data (two images)
As in Turmon et al. 2002; Turmon et al. 2010
• Data series is Marmask_720s, derived using M_720s and Ic_720s
• Pipeline module yields 4096x4096 BITPIX=8 masks, in image coordinate frame, with WCS and links to source data.
May 2010 – Mar 2011 available through JSOC
About 35s to compute per mask
References
M. Turmon, H. Jones, J. Pap, O. Malanushenko, “Statistical feature recognition for multidimensional solar imagery”, Solar Physics, 2010.
H. Jones, G. Chapman, K. Harvey, J. Pap, D. Preminger, M. Turmon, S. Walton, “A comparison of feature classification...”, Solar Physics, 2007.
M. Turmon, J. Pap, S. Mukhtar, “Statistical pattern recognition for labeling solar active regions: Application to SoHO/MDI,” ApJ, 2002, 396-407.
The mixture modeling work appeared in:
Mixtures-2001, “Recent Developments in Mixture Modelling,” Hamburg.
Compstat-2004, Prague, as “Symmetric Normal Mixtures”
National Aeronautics and Space Administration
Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadena, California
Status and Plans
• Masks are usable, and routinely computed in the pipeline.We hope to separate umbra/penumbra and identify faculae (we await limb-darkening-removed Ic data series)
• Tracks are not yet being computed as part of pipeline production, but we expect them by July.Remaining issues are tracker parameter tweaks and software integration details.Technical improvements: remove length-1 tracks from cosmic rays, automatically flag gross tracking errors, link to NOAA AR numbers, compute overall track quality metrics, visualize tracks in summary form.
Active Region Grouping• Mask is a set of pixels; we wish to group clumps of pixels into
NOAA-like ARs
• Formal literature on this problem is not well-developed
• We have used a matched filter approach with an elongated Gaussian kernel
• Mpatch_720s data series has instantaneous patches
May 2010 – Mar 2011 available through JSOC
About 30s to compute per mask
MDI Labeling
2002 Sep. 02, 11:11 UTC
Convolved with Template
Identified Groups
Grouping Example Grouping Details
• Gaussian in 3D pixel-pixel distance, stretched longitudinally• FWHM ~50x25Mm (~150x75 HMI pixel) at disk center• Convolution on sphere to treat the limb fairly• AR masks are sparse: allows fast convolution (HMI: 12s)
Active Region Tracking• Core idea: Associate ARs in
before (B) and after (A) images
• Correlation-based tracker
• Standard latitude-dependent motion model
• Compute area-of-overlap of AR patches found above
• Overlap between a in A and B in B is D(a,b)
• Solve linear assignment problem to match A up to B:
where P is a permutation matrix giving the B-to-A mapping
Fast, exact solution by linear programming
• Chaining this association across many frames yields tracks
• Tracks can be finalized only after they are unseen for a given time
AB
Before After
Track Data Characteristics• Have tracks for May 2010 – April 2011.
• 12 months, 43000 masks, 563 tracks.
• Median length 150 frames.76 tracks of length 1 (cosmic rays)28 are ≥ 12 days (1440 frames)
Examples of Tracked Frames
• These are flipped N-S. Apologies!
• HMI frames were reduced to 1024x1024,and the data series was downsampled 120xto one frame per day.
• February 2011 flaring AR is in orange.
• Yellow AR resulted from several merges; some can be seen in these frames.
• Numerous small ARs died after frame 1;red AR died after frame 5.
Mask Algorithm• Bayesian approach trades off pixel-by-pixel agreement of mask
value to the data versus spatial coherence of labels
Limited usage of spatial information to disambiguate cases near the class boundary.
Smoothness takes spherical geometry into account, see Turmon et al. 2010 for details
• Mask is found by discrete optimization of the above posterior
• Current region models do not use intensity information
Flattened Ic not quite ready
Mask Models and Examples• Histogram (below) shows pixels
(shaded gray) near critical |B| switch classes due to neighbors
• Sample mask (right) shows fine spatial structure is preserved.
Mask: 2011/02/14 12:00
zoom
Histogram of Track Lengths
Track Length (in frames)
Kernel atDisk Center
The HARP Data Product• HARPs are indexed by HARP number, analogous to NOAA
AR number, and time (T_REC)
• For a single HARP, the data series is a list of patches containing the entire disk passage of the HARP.The HARP is extrapolated backward and forward in time using differential rotation formulae so that the entire disk passage is captured, even if the HARP is not visibly active.
• Each patch in the list is a rectangular cutout from the imager plane. A patch can be overlaid on a full-disk image (e.g., from M_720s) by a simple coordinate shift.
• See the banner at the top of the poster for an example.
• The footprint covered by the patch is determined by the the smallest lat/lon bounding box that encompasses all appearances of that particular HARP. See diagram below.
• The animating idea is that the observer is hovering over the AR, staying at a constant latitude and moving at a constant angular rate in longitude.
Kernelat Limb
50 MmExampleAR Mag.(for scale)
ExampleAR Mask
B (gauss) Re
lativ
eF
requ
enc
y
Tracked Patches of Solar Activity for HMIMichael Turmon (JPL/Caltech), Xudong Sun (Stanford), Todd Hoeksema (Stanford)
1
Time Sam
e lat/lon
