Feature Classification of NSO/Kitt Peak Magnetograms

1. Feature Classification of NSO/Kitt Peak Magnetograms O.V. Malanushenko (APO/SDSS), H.P. Jones (NSO), J.M. Pap (UMBC/GEST), and M.J. Turmon (JPL) Figure 2. Top panel shows quiet pixel count versus time. Middle panel shows disk fraction versus time for active network (white), active region (red), and sunspot (green). Bottom panel shows sunspot disk fraction for two methods of building per-class models: histograms computed directly from training data, and Gaussian mixture models fitted to the training data. The difference is in green. • Statistical Image Segmentation • We have extended the method of Turmon, Pap, and Muktar (Astrophys. J. 586, 396; 2002) which labels quiet Sun, faculae, and sunspots using MDI magnetograms and photograms. Our adaptation seeks more features and uses NASA/NSO Spectromagnetograph (SPM) or SOLIS Vector Spectromagnetograph (VSM) observations with higher dimensionality. Characteristics of the method include: • Training the method with a subset of independently labeled observations • Computation of class-conditional probabilities from the training set using Gaussian mixture models or direct histogram interpolation • Use of Bayes’ rule to invert class-conditional probabilities to find probabilities that each pixel is a member of each feature class given its observed properties • Simulated annealing to find the image segmentation with maximum global probability • Spatial smoothing accomplished through models of Bayesian prior probabilities Classification Time Series The time series provide two sorts of information. First, they tell us how consistent the labeling is across time. Second, they can be linked to irradiance time series for further validation purposes, and to understand the links between visible activity and irradiance. We concentrate on the first purpose. The quiet sun pixel count time series is useful for diagnostic purposes, to identify and screen erroneous images from consideration. This can identify gross problems such as partial full-disk data. In conjunction with the other plots, we also conclude that such extensive (in time) classifications using the magnetograms and, especially, intensity images, is well-founded. It appears that extraneous systematic effects will not overwhelm the signal. The increase in network with the solar cycle may signal confusion between network and active regions in our current classification. We see evidence of this in individual images and believe that it is due to overly restrictive thresholds for full-disk training data. We believe that we may improve these thresholds by visually identifying separate partial-disk areas where active regions and network are minimally mixed . As predicted by our earlier work at AGU, there are small but significant differences in the sunspot identifications provided by the histogram versus the mixture method. As our models are refined (see discussion) we will investigate this discrepancy more fully. 04 Nov 1998 05 Nov 1998 06 Nov 1998 Summary conclusions Threshold Training Segmentations • Data reduction and analysis software and hardware is in place to perform large-scale analyses. • Images, specifically continuum intensity, are clean enough to apply these methods across timescales of years. • Network class as existing seems to be contaminated with active region pixels. • Existing threshold-based models should be refined to eliminate the arbitrary thresholds to the extent possible. For instance, plentiful quiet areas are available which can be used to define the quiet class more objectively. Magnetic & Intensity Thresholds • To study the performance of the statistical method for smaller-scale representations of network, we have used a simple magnetic-intensity threshold algorithm to label a training set of images. • Quiet Sun: |B| < 40 Gauss • Network: 40 < |B| < 130 • Active Region: |B| > 130 and not sunspot • Sunspot: I/I(center-to-limb) < 0.93 and |B| > 300 • The top row of images in Figure 1 shows this segmentation for three images in the training set. Network flux tends to be concentrated in small patches, presumably located on cell boundaries of chromospheric network as might be seen for example in Ca II K. A continuous string-like delineation of network boundaries is usually not seen with this labeling method except in areas of higher flux concentration. • The lower two rows of Figure 1 show statistical segmentations for the same days using Gaussian mixture models derived from the entire training set with different degrees of spatial smoothing. Visually, both methods agree very well with the training labelings. Statistical: Gaussian Mixture Probabilities; No Smoothing • Future Work • Identify and exclude erroneous images. • Improve classification with better training data. • Use singular spectrum analysis on time series to identify unexplained signals (e.g., beyond annual and 27-day periodicities). • Apply to SOLIS/VSM observations. • Extend to entire SPM data set. Statistical: Gaussian Mixture Probabilities; Moderate Smoothing Acknowledgements This work is supported by NASA/Solar and Heliospheric Physics grant SHP04-0037-0099. Figure 1. Top: Training segmentations derived by applying thresholds to SPM intensity images and magnetograms. Middle: Statistical segmentations with no spatial smoothing using Gaussian mixture models. Bottom: Same as middle with moderate spatial smoothing. Quiet Sun--blue; Network--aqua; Active Region--yellow; Sunspot--orange.