I pixel using a cosine correction factor (McAteer et al., 2005a). After this stage, B t−∆t data is differentially rotated to time t to correct for feature motions due to solar rotation using the latitudinal dependence derived in Howard et al. (1990).
The corrected magnetograms are made binary by setting all pixels with magnetic field values above the ±70 G threshold equal to one, yielding masks M t−∆t and M t. Features consisting of less than 50 pixels and those which are not present in both masks are removed by the following operations (Figure 3 C). Firstly, each mask is dilated by 10 pixels to allow for region expansion. Secondly, the binary masks are subtracted such that non-zero pixels in the difference mask identify features only occurring in M t−∆t or M t. These transient features are subsequently removed from the un-grown version of M t, which is then dilated by 10 pixels to form M f,t (Figure 3 D). Individual contiguous features in M f,t are indexed by assigning ascending integer values (beginning with one) in order of decreasing feature size. The segmentation output is an indexed grown mask (IGM t ), as shown by the thick red box in Figure 2.

Characterization
The aim of SMART is to characterize ARs in a manner which does not make theoretical assumptions or require many observations of the same feature. Our design is adaptable, so that the software may produce initial results in near-realtime for operational purposes, but allows the retrospective addition of complex property measurements (e.g., magnetic helicity). These requirements define criteria for the selection of initial property calculations. There are many AR properties that may be derived from magnetograms. A subset of these are derived from 96 minute LOS data and those output by SMART are included in Tables 1 and 2. The SMART characterization process utilizes the feature mask retrieved by the methods outlined in the previous section, following the procedure detailed in Figure 6. The property measurements are derived from the mag- netogram taken at time, t which is processed in the manner detailed below. We subscript the mask containing all features by i to extract a single feature mask, IGM t,i. A cosine-weighted area map, A cos,t,i is derived which corrects pixel areas to solar surface area rather than plane-of-sky area, and is summed to yield total feature area, A tot,t,i.
Full-disk magnetograms B t−∆t and B t are processed as in Section 2.1 (thresholding, LOS correction, B t−∆t differentially rotated to time t), but without smoothing. Single features are extracted for magnetic property determination using the indexed grown mask, Data Type Identifier Explanation Feature Array M P SL,t,i polarity separation line mask M P SL,thin,t,i thinned polarity separation line mask Property Value yielding an array where all pixels but those in the feature are set to zero. The processed magnetograms are subtracted and divided by their time separation to yield a map of the temporal change in field strength, dB/dt| t, leading up to time t. This is combined with A cos,t,i to determine the flux emergence rate, dΦ/dt| t,i, of feature i.
The extracted B t,i is used to extract other properties from feature i (as detailed in Table 1) such as statistical moments of the magnetic field and the minimum and maximum magnetic field values (B min,t,i and B max,t,i ). B t,i is multiplied by A cos,t,i to derive the total positive, negative, and unsigned flux (Φ +,t,i, Φ −,t,i, and Φ uns,t,i ), the relative flux imbalance (Φ imb,t,i ), and the net flux emergence rate (dΦ/dt| net,t,i ).
The extracted feature magnetogram, B t,i, is also used to derive properties based on the polarity separation line (PSL). Figure 7 summarizes the extraction of feature properties related to PSLs and Table 2 lists the properties derived. Initially, the feature is segmented into its positive and negative components. These components are used to create a positive and negative mask, each of which is dilated by 4 pixels. The two masks are summed and the region of mask overlap becomes the PSL binary mask, M P SL,t,i. The algorithm then thins M P SL,t,i to one pixel (M P SL,thin,t,i ) and sums the non-zero pixels to determine the PSL length (L P SL,t,i ). L sg,t,i is obtained by summing only those pixels which have ∇B t,i > 50 G Mm −1, where ∇B t,i is calculated by numerical differentiation using 3-point Lagrangian interpolation. We also calculate the R-value (R t,i ) as presented in Schrijver (2007) and the W L sg gauge (W L sg,t,i ) as presented in Falconer et al. (2008), both of which use specific gradient and magnetic field thresholding when extracting the PSL. Finally, using M P SL,t,i we calculate R * t,i which is a more sensitive version of the R-value, since it contains no gradient thresholding and the magnetic field threshold of ±70 G is much lower than the ±150 G used in Schrijver (2007). The algorithm convolves M P SL,t,i with a 20×20 pixel 2 kernel containing a 2D gaussian with a FWHM of 10 pixels, which is multiplied by B t,i and summed to achieve R * t,i. Similarly, an alternative of W L sg,t,i, W L * sg,t,i is calculated by applying the Falconer et al. (2008) method, but using a magnetic field threshold of ±70 G and no gradient threshold. A set of data structures is created for each magnetogram including the above mentioned properties of each extracted feature (used for classification; Section 2.3) and the feature's heliographic location and time of measurement (used for feature tracking; Section 3).

Classification
At this stage the SMART algorithm has characterized the properties of each automatically extracted feature. The classification process uses these properties to discriminate between various feature types, which are saved in the algorithm output. Extracted features are initially grouped (as shown in Figure 8) into two catagories: features with a flux imbalance greater than 90% are classified unipolar (U), while those having less than 90% are classified multipolar (M). After polarity balance, the total unsigned magnetic flux (Φ uns,t,i ) is tested. Features with Φ uns,t,i greater than 10 21 Mx are classified as large (L), while features with Φ uns,t,i less than 10 21 Mx are classified as small (S). Finally, the sign of dΦ dt | t,i is tested to determine if features are increasing in flux (emerging, E) or decreasing in flux (decaying, D). The classification scheme results in eight possible feature classifications which are then also attributed to common magnetic feature designations: MLE and MLD are denoted evolving ARs; MSE and USE are denoted emerging flux concentration (EF); MSD, USD, and are denoted decaying flux concentrations (DF), and finally, ULE and ULD are denoted plage (PL). These common designations are also saved in the algorithm output, allowing one to make a quick assessment of which regions on disk are interesting from a monitoring point of view. For example, EFs may become ARs and evolving ARs may produce activity during their evolution, while PL and DF are not likely to produce activity.

Tracking
Having detected various solar features, the SMART algorithm associates features across different time intervals. Spatial and temporal information is used to track features between consecutive images (Section 3.1) and around the far-side of the disk between consecutive solar rotations (Sections 3.2). Features are then cataloged using the time of their first detection and their classification (Section 3.3).

Consecutive Images
The set of features in a magnetogram is compared with the previous five magnetogram sets to associate previously catalogued features with the current set. Feature positions (HG pos,t,i ) are differentially rotated, using the latitudinal dependence derived in Howard et al. (1990), to the same time t and features matched when their heliographic separations are less than 5 degrees. Features having one classification in previous sets may be associated with features having a different one in the current set. Thus, the SMART algorithm is capable of tracking possible ARs (MLE, MLD) back to their first emergence as an EF (MSE). Decaying features may also be associated with features previously denoted as possible ARs, allowing ARs to be followed through their final stages of evolution. Fragmentation often occurs in these late stages which SMART allows for since it does not preclude multiple features from being associated with a single previous feature. If one feature splits into two, each resulting fragment will be associated with the original feature if the resulting fragment positions are within the matching threshold of the original. A letter is appended to the catalog name of each additional associated feature so that individual fragments may be differentiated.

Far-side Passage
Features are tracked beyond the limb through multiple solar rotations to study their evolution from emergence to decay. We calculate the rotation period, P rot,i, for each feature at time t, which depends on its heliographic latitude due to differential solar rotation. The feature position is compared to those in the five magnetogram sets centered on time (t + t 70 )