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| == Overview == |
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| The corresponding data product is hmi.Marmask_720s. There is a corresponding near real time data product, | The corresponding data product is hmi.Marmask_720s. There is a corresponding near-real-time data product, |
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| HARP is an HMI Active Region Patch that is identified in one or more HMI line-of-sight magnetograms. HARPs may be observed over an extended time interval. Each HARP has a serial number, the HARP_ID, that will often be linked with a NOAA Active Region. This data series provides pointers to information covering the entire disk passage for each HARP in the HMI catalog. | The mask is an integer (char, or BITPIX = 8) value at each pixel. Currently the integer can be one of three values: 0 for off-disk, 1 for quiet, and 2 for magnetically active. We hope to add a facula class, especially as the intensity images become more well-calibrated. |
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| The data series tracks patches by associating information from other data series using links to maps and parameters computed for patches identified in individual magnetograms and intensity images. There are no actual data segments, just links to vector, line-of-sight, and intensity data and associated keywords. | We use two image series, magnetograms (from hmi.M_720s) and intensitygrams (from hmi.Ic_720s), to determine class membership. This first iteration of the masks concentrates on active regions, so we rely on the magnetograms almost exclusively to compute the masks. The mask image is computed and stored in "focal plane" or image coordinates, so mask values at a given ''(i,j)'' pixel correspond with magnetic activity at that ''(i,j)'' pixel. == Methodology == The methods used were developed by Turmon, Pap, and Mukhtar (), and subsequently refined to allow for spherical geometry (). The basic idea is that the mask should optimize a function containing two terms, one for pixel-by-pixel agreement of the observed ''(M,Ic)'' values to what is expected for a given class, and another for smoothness across adjacent pixels. The first term, which matches observed ''(M,Ic)'' values to the expected scatter for each class, is dominant in the calculation. The smoothness term only affects mask values that are near the boundary between classes (i.e., have an ''(M,Ic)'' value that is inconclusive). Geometrical information is encoded in the smoothness term. The overall effect does not correspond to a threshold rule on ''M'' and ''Ic''. Boundaries between classes follow non-axis-parallel contours that are a property of the competition between the expected scatter of the two classes. The method is implemented in a pipeline module called hmi_segment_module. It requires about 40 seconds per 4096x4096 image. == Example Images == |
ARmask - HMI Active Region Full-Disk Masks
Overview
This is a full-disk data product that currently identifies magnetically active pixels. The corresponding data product is hmi.Marmask_720s. There is a corresponding near-real-time data product, which is used to get timely space weather information, called hmi.Marmask_720s_nrt.
The mask is an integer (char, or BITPIX = 8) value at each pixel. Currently the integer can be one of three values: 0 for off-disk, 1 for quiet, and 2 for magnetically active. We hope to add a facula class, especially as the intensity images become more well-calibrated.
We use two image series, magnetograms (from hmi.M_720s) and intensitygrams (from hmi.Ic_720s), to determine class membership. This first iteration of the masks concentrates on active regions, so we rely on the magnetograms almost exclusively to compute the masks. The mask image is computed and stored in "focal plane" or image coordinates, so mask values at a given (i,j) pixel correspond with magnetic activity at that (i,j) pixel.
Methodology
The methods used were developed by Turmon, Pap, and Mukhtar (), and subsequently refined to allow for spherical geometry (). The basic idea is that the mask should optimize a function containing two terms, one for pixel-by-pixel agreement of the observed (M,Ic) values to what is expected for a given class, and another for smoothness across adjacent pixels. The first term, which matches observed (M,Ic) values to the expected scatter for each class, is dominant in the calculation. The smoothness term only affects mask values that are near the boundary between classes (i.e., have an (M,Ic) value that is inconclusive). Geometrical information is encoded in the smoothness term.
The overall effect does not correspond to a threshold rule on M and Ic. Boundaries between classes follow non-axis-parallel contours that are a property of the competition between the expected scatter of the two classes.
The method is implemented in a pipeline module called hmi_segment_module. It requires about 40 seconds per 4096x4096 image.