March 9, 2017 02:48:18

Solar Event Tracking and Interpolation

Solar Events Tracking

This work introduces the use of an appearance model based on sparse coding. This model is used to classify solar event detections as either the same detected event at a later time or an entirely different solar event of the same type. The output of such classification tasks can be adapted to be a component of the tracking algorithm known as multiple hypothesis tracking. The advantage of the presented model is that it learns the appearance of the known event online and then generates the matching likelihood for each of the choices presented to it. This online learning is crucial to eliminate reliance on previous tracking information, which does not exist for many solar event types. We show that this online method preforms as well, or better in the task of differentiation than the offline learning of appearance on solar events.

 

Solar Events Interpolation

One of the main challenges that solar physicists face in their research is the scarcity of accurate event trajectory metadata. Even though solar images are captured relatively frequently, after the initiation if SDO’s FFT modules, the detections are not run on all solar images and thus report solar events metadata less frequently. One way to mitigate the scarcity problem is to interpolate the missing trajectory data with a given cadence based on the originally reported solar events in the HEK.

A number of interpolation strategies were designed depending on the event type to be interpolated. The simplest interpolation method is MBR-interpolation that was designed for event types that are reported using their minimum bounding rectangles such as emerging flux and flares. FI-interpolation is another interpolation method that includes the unique physical characteristics of the filament event type to make it more specialized. For most of the event types, CP-interpolation is adopted which uses centroid shape signature along with dynamic time warping alignment to deal with complex geometries. In addition to interpolating trajectory data, extrapolation is also used to estimate the shape of the geometries that do not belong to any trajectory.