Mathematics – Logic
Scientific paper
May 2009
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009spd....40.0921z&link_type=abstract
American Astronomical Society, SPD meeting #40, #9.21; Bulletin of the American Astronomical Society, Vol. 41, p.820
Mathematics
Logic
Scientific paper
NOAA active region catalog has been consistently providing information of the sunspots for several decades. The catalog is widely used by the solar physics community for various purposes. It provides the basic referencing information of an active region's unique identification number and its location. It uses the MacIntosh scheme to classify the morphological properties of sunspots. It also provides Mt Wilson magnetic classification of sunspots. However, the catalog does not capture the rich information of active regions. In particular, the prediction of potential activities (flares and CMEs) requires a large set of quantitative attributes of active regions. We will present our effort on creating a comprehensive digital active region catalog from SOHO/MDI observations since 1996. Using advanced image processing technique, we are able to automatically identify and isolate the active regions from input images. For each identified active region, we obtain the basic parameters, including magnetic field intensity, area, flux and centroid locations for positive, negative, signed and unsigned polarities. Further, we effectively decompose the active region to multiple positive and negative fragments (corresponding to multiple sunspots in the regions) and obtain the basic parameters of each fragment. To further quantify the active region complexity or energetics, we apply multi-pole expansion technique to obtain the moments of the 2-D active region, including monopole, dipole, quadruple, octopole, hexadecipole and higher. We have also developed an effective method to identify and quantity the polarity inversion lines (PILs) in an active region, including the number of PILs, their location, length and strengths. This automated method can be used to batch-process the data, and thus improve the efficiency of validation. The consideration of the optimization on the thresholding parameters will be discussed. The statistical distribution of these parameters will be presented.
Robinette S.
Wang Yadong
Zhang Jie
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