• ARmaskDataSeries

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 also a near-real-time data product, which is used to get timely space weather information, called hmi.Marmask_720s_nrt.

The Marmask data products are indexed by T_REC.

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 use two image series, magnetograms (from hmi.M_720s) and intensitygrams (from hmi.Ic_noLimbDark_720s), to determine class membership. We have concentrated on magnetically 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 noted at that (i,j) pixel.

Methodology

The methods used were developed by Turmon, Pap, and Mukhtar (ApJ, 2002, 568(1), 396-407), and subsequently refined to allow for spherical geometry (Solar Phys., 2010, 262(2), 277-298).

The core 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.

As noted above, the current processing uses the magnetogram almost exclusively. The plot below shows the distribution of quiet Sun pixels (blue line) and magnetically active pixels (green line) versus magnetic field (x axis, in HMI Gauss). The gray areas are pixels whose classification can be changed if the spatial cues in the objective function indicate that it would be beneficial enough.

http://sun.stanford.edu/~turmon/jsoc-wiki/classhist2-2011-feb-14-small.png

The method is implemented in a pipeline module called hmi_segment_module. It requires about 40 seconds per 4096x4096 image.

Example Images

This is a labeling and the corresponding magnetogram from 2011 February 14.

http://sun.stanford.edu/~turmon/jsoc-wiki/mask2011-02-14.png http://sun.stanford.edu/~turmon/jsoc-wiki/mag2011-02-14.png

This is a detail from the same mask image. It shows the structure at fine spatial scales.

http://sun.stanford.edu/~turmon/jsoc-wiki/mask2011-02-14-zoom.png

Keywords

Besides standard HMI image keywords (observational geometry, WCS, observation time), we set some mask-specific keywords.

Using the following keywords will allow your code to continue to work even if the mask value for the Active class changes in the future. Currently, OFFDISK is 0, QUIET is 1, and ACTIVE is 2, but this is likely to change. Also, we find a quality measure, ARM_QUAL, which is currently nonzero only in the event of evident trouble at the solar limb. It is a bit-field which is analogous to QUALITY for level 1 images.

We also find some mask summary statistics, such as the number of pixels declared to be active and their total line-of-sight flux. These can be useful for screening images.

Finally, we record some algorithm parameters for reproducibility. These should be of interest only to developers and operators.

Related Data Series

• HARPDataSeries